Below are 100 interview questions focused on Generative AI, covering fundamental principles, classical generative modeling techniques, popular architectures (e.g., GANs, VAEs), transformer-based models (large language models), diffusion models, and advanced applications. The questions progress from foundational concepts to cutting-edge approaches and considerations in the field.
Answer:
Generative AI refers to models and techniques that create new content—images, text, audio, or other data—resembling the patterns of the training data. Instead of just predicting labels, generative models learn data distributions and can sample from them to produce novel instances. It underpins innovations like deepfakes, text-to-image synthesis, and large language models.
Answer:
- Generative Models: Learn the joint probability distribution p(x) or p(x, y). They can generate new samples similar to training data.
- Discriminative Models: Learn decision boundaries or conditional distributions p(y|x) for classification or regression. They do not generate new samples but predict labels given inputs.
Generative models create data, while discriminative models classify or predict from data.
Answer:
Classical generative models include Gaussian Mixture Models, Hidden Markov Models, and Naive Bayes. They rely on hand-crafted probability distributions or graphical models. While useful in certain domains, they often struggle to capture complex, high-dimensional data as effectively as modern deep generative models.
Answer:
Latent variable models assume observed data is generated from latent (unobserved) factors. By introducing latent variables z, these models define p(x, z) and integrate out z to get p(x). Examples include Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) where a latent code controls the generation process. Latent spaces capture abstract features, enabling meaningful manipulation of generated samples.
Answer:
A VAE is a generative model that encodes data into a latent space distribution and learns to reconstruct it from a latent code. It optimizes a variational lower bound, combining a reconstruction term (ensuring generated samples resemble input) and a KL divergence term (regularizing latent space structure). By sampling from the learned latent distribution, VAEs can generate new data points similar to the training set.
Answer:
Autoencoders compress and reconstruct data deterministically, producing point estimates in latent space. VAEs model latent variables as distributions (mean and variance) and sample from these distributions. This probabilistic approach enables generating new samples by sampling from latent distributions, providing a well-defined generative process not present in standard autoencoders.
Answer:
GANs involve two networks: a Generator (G) that creates fake samples and a Discriminator (D) that distinguishes real from fake. They engage in a min-max game: G tries to fool D by producing realistic outputs, and D tries to detect fakes. Over training, G learns to generate data indistinguishable from real examples, making GANs powerful and flexible generative models.
Answer:
- VAEs: Learn a latent space distribution, optimizing a reconstruction + regularization objective. They produce smooth latent spaces and probabilistic generation, but may produce blurrier samples.
- GANs: Use an adversarial loss, often producing sharper, more realistic outputs. However, they may lack explicit latent variable inference and stability of training can be more challenging.
Answer:
Mode collapse occurs when the generator produces a limited variety of outputs, ignoring other modes of the data distribution. Instead of generating diverse samples, it sticks to a few “safe” solutions. This reduces coverage of the real data distribution. Techniques like minibatch discrimination, feature matching, or Wasserstein GANs help mitigate mode collapse.
Answer:
WGAN replaces the standard GAN discriminator with a “critic” that approximates the Earth Mover (Wasserstein) distance between real and generated distributions. By providing smooth, continuous gradients, WGANs reduce training instability and mode collapse. The critic scores quality and diversity rather than providing a binary classification, improving training dynamics.
Answer:
DCGAN applies CNN architectures to GANs, using strided convolutions in the discriminator and fractional-strided (transpose) convolutions in the generator. This design stabilizes training and yields high-quality image generation. DCGAN is a milestone that made GAN training more reliable and accessible.
Answer:
cGANs provide additional conditioning information (e.g., class labels) to both generator and discriminator. This enables controlled generation: generating specific categories of images or other attributes. cGANs guide the generator towards desired outputs, improving diversity and usefulness in targeted applications.
Answer:
A class-conditional image generator takes an input label (e.g., “dog”) and noise to produce a dog image. The discriminator receives the image and label, ensuring that the generated image matches the given class. This allows fine-grained control over generated content, useful for data augmentation or custom image creation.
Answer:
Pix2Pix is an image-to-image translation framework using a cGAN. Given a paired dataset (e.g., sketches and corresponding photos), Pix2Pix learns a mapping from input domain to output domain. It transforms edges into photos, daytime images to nighttime images, and so forth, enabling versatile style and content transformations.
Answer:
CycleGAN performs unpaired image-to-image translation. It learns mappings between two domains without one-to-one pairs. By enforcing cycle consistency (converting an image from domain A to B and back should yield the original image), CycleGAN aligns the distributions of two datasets. This enables translation (e.g., horses ↔ zebras) without paired training samples.
Answer:
- PixelRNN/PixelCNN: Model images pixel-by-pixel, predicting each pixel given previously generated pixels. They yield a tractable likelihood and stable training but are slow at generation.
- GANs: Generate the entire image in one go, often faster and producing sharp images but without likelihood estimation and can be harder to train. Both are generative but differ in inference speed, training stability, and likelihood modeling.
Answer:
Normalizing Flows define invertible transformations mapping simple distributions (like Gaussian) to complex data distributions. They allow exact likelihood computation and efficient sampling. Models like Glow produce high-quality image samples and enable manipulation of latent variables with a mathematically tractable framework.
Answer:
Diffusion models gradually add noise to data and learn to reverse this process, denoising step-by-step to generate samples. By modeling the distribution of data as a progressive denoising process, they achieve state-of-the-art image quality and diversity. Diffusion models (e.g., DDPM, Stable Diffusion) have become popular for high-fidelity image generation.
Answer:
- Diffusion Models: Stable training, produce diverse, high-quality samples, but can be slow to sample due to iterative refinement.
- GANs: Very fast generation but prone to mode collapse and training instability. Diffusion models offer more controllable and theoretically grounded sampling, but at a higher computational cost.
Answer:
Stable Diffusion is a popular text-to-image diffusion model that generates high-quality, diverse images from text prompts. It can run efficiently on consumer GPUs due to latent diffusion techniques. Its open-source nature democratized access to advanced image generation, spurring innovation and community-driven improvements.
Answer:
Latent Diffusion Models apply the diffusion process in a latent space (from a pretrained autoencoder) rather than on raw pixels. This reduces computational complexity and speeds up sampling. By working in a lower-dimensional latent space, they preserve fidelity and detail while enabling powerful capabilities like text-conditioned image generation.
Answer:
LLMs (e.g., GPT-3, GPT-4) are large autoregressive transformers trained on massive text corpora. They learn complex language patterns and can generate coherent, contextually rich text. As generative AI models for language, they produce essays, code snippets, answers, and creative writing, enabling chatbots, assistants, and content generation at scale.
Answer:
Prompt engineering involves crafting the input text (prompts) to guide LLMs toward desired outputs. By carefully choosing words, instructions, or examples, users can influence the model’s style, format, and correctness. Effective prompts can drastically improve results without changing the model’s parameters.
Answer:
- Zero-Shot: The model performs a new task without explicit training examples for that task, relying on general knowledge.
- Few-Shot: Minimal examples (e.g., a few input-output pairs) are provided in the prompt.
LLMs leverage their pretrained knowledge to generalize and solve tasks with minimal or no additional labeled data.
Answer:
RAG models retrieve relevant documents from an external corpus and feed them to the generator. The LLM then conditions its response on these retrieved facts, reducing hallucinations. By grounding answers in real data, RAG ensures better factual accuracy and up-to-date information.
Answer:
CTC models are often used in speech recognition. They learn to map input sequences (audio features) to output sequences (transcriptions) without requiring aligned labels. Although primarily for recognition, such sequence-to-sequence predictions can be adapted or extended to generative tasks like TTS (Text-to-Speech).
Answer:
- Autoregressive (WaveNet): Generates samples one by one, capturing complex audio structures. High-quality but slow sampling.
- Diffusion (WaveGrad): Uses iterative denoising steps. Stable training and can produce natural audio.
Both produce high-fidelity audio; diffusion offers better training stability and sometimes faster parallelizable generation steps.
Answer:
Modern TTS systems use models like Tacotron or VITS, which map text to mel-spectrograms and then synthesize waveforms with vocoders (WaveGlow, HiFi-GAN). They learn end-to-end to produce natural-sounding speech from text. Autoregressive or flow-based components generate realistic, intelligible, and expressive audio.
Answer:
Tokenizers split text into tokens (words, subwords). The model generates sequences of tokens. The chosen tokenization method (BPE, SentencePiece) affects vocabulary size, OOV handling, and generation quality. Good tokenization ensures that the model can produce coherent, diverse language efficiently.
Answer:
Metrics:
- Inception Score (IS): Measures how “distinct” and “realistic” generated images look.
- FID (Fréchet Inception Distance): Compares generated and real image feature distributions. Lower FID means closer to real data distribution.
- Precision and Recall for Generative Models: Evaluate coverage and quality in the generated set.
Answer:
- Perplexity: Measures how well the model predicts text. Lower perplexity means better language modeling.
- BLEU, ROUGE, METEOR: Compare generated text to references in tasks like machine translation or summarization.
- Human Evaluation: Ultimately, human judgments assess fluency, coherence, and appropriateness.
Answer:
Guided sampling biases the sampling process towards certain attributes or conditions. For example, classifier guidance uses gradients from a classifier to steer the diffusion model’s denoising steps. This lets users control style, content, or class of generated images while retaining the quality of diffusion generation.
Answer:
In VAEs, to backpropagate through stochastic sampling, we sample latent variables z = μ + σ * ε, where ε ~ N(0,1). This separates randomness from model parameters, allowing gradients to flow through μ and σ. The trick makes learning latent distributions differentiable.
Answer:
Flow-based models define invertible transformations from simple distributions (e.g., Gaussian) to complex data distributions. Because transformations are invertible, these models can compute exact likelihood and easily sample. They often produce high-fidelity images and enable latent space manipulations like interpolation.
Answer:
Temperature scales the logits before softmax. Temperature < 1 makes predictions more confident, producing less diverse outputs. Temperature > 1 broadens the distribution, encouraging more exploration and creativity but potentially reducing coherence. Adjusting temperature balances control and diversity in generated text.
Answer:
StyleGAN and StyleGAN2 are GAN architectures for high-resolution image generation, introducing style-based latent spaces and progressively growing layers. They produce incredibly realistic faces and allow controlling attributes (e.g., hair, lighting) via latent directions. StyleGAN2 improves stability, quality, and detail over the original StyleGAN.
Answer:
Latent directions are vectors in the latent space that consistently change certain attributes (smile, hairstyle, background) when applied to latent codes of generated images. This makes controlling generated images intuitive—simply manipulate latent directions to achieve desired visual changes without retraining the model.
Answer:
ControlNet conditions image generation on additional input signals (like edges, segmentation maps) while using a pretrained diffusion model. By injecting structured guidance, ControlNet influences composition and style, enabling users to control output more precisely. It extends the flexibility of models like Stable Diffusion.
Answer:
Generative AI can create deceptive media (deepfakes), violating privacy, enabling misinformation, or unauthorized content generation. Ethical considerations include the need for watermarking, detection tools, consent, legal frameworks, and responsible usage guidelines. Balancing creativity with misuse prevention is crucial.
Answer:
- Fact Checking: Integrating retrieval from authoritative sources.
- RAG setups: Condition generation on retrieved documents to reduce fabrications.
- Calibration: Use uncertainty estimates, and instruct the model to say “I don’t know” when unsure.
- Fine-tuning on fact-checking tasks or using models specialized in truthfulness.
Answer:
Usually, autoregressive models are unidirectional. Bidirectional autoregressive models (e.g., masked LMs like BERT) consider all positions simultaneously, but they do this by masking tokens and predicting them. While beneficial for representation, pure bidirectional models are less straightforward for text generation tasks compared to unidirectional models.
Answer:
LLMs often rely on attention-based transformers with large context windows. Techniques like hierarchical modeling, memory tokens, or recurrence can extend context. Still, handling extremely long documents challenges model capacity and efficiency. Research explores methods like retrieval augmentation and segment-wise processing.
Answer:
- Decoder-only (e.g., GPT): Predicts the next token from previously generated tokens. Good for text completion.
- Encoder-Decoder (e.g., BERT-based seq2seq): Encodes input sequences into a latent representation and decodes outputs. Good for translation, summarization, where a separate input sequence conditions the generation.
Answer:
Prompting uses instructions or few examples in the input to guide a pretrained model to perform tasks without additional parameter updates. This exploits the model’s learned knowledge. By carefully engineering prompts, users can achieve reasonable performance on new tasks, avoiding expensive fine-tuning.
Answer:
Multimodal generative models learn joint distributions over multiple data modalities (e.g., text and images, audio and video). They generate coherent cross-modal outputs (like producing an image from a text prompt). Models like DALLE, Stable Diffusion with text prompts are multimodal, integrating vision and language.
Answer:
Autoregressive image models (PixelCNN, PixelRNN) generate images pixel-by-pixel or channel-by-channel in a fixed order. They estimate p(x) by decomposing it into a product of conditional distributions. Although exact likelihood is tractable, such generation is slow and complex, leading to popularity of faster models like GANs and diffusion.
Answer:
Likelihood estimation provides a principled metric to assess how well a model fits the data distribution. Models like VAEs, normalizing flows, and PixelCNN can compute or approximate likelihood, guiding model selection and hyperparameter tuning. Likelihood-based methods offer quantitative comparisons beyond just sample quality.
Answer:
Fine-grained control involves specifying style, tone, or content details in prompts. For instance, instructing an LLM: “Write a summary in a formal style” or “Generate code comments in Python.” Another approach is conditioning models with special tokens or context documents that bias the generation toward desired characteristics.
Answer:
- Greedy Decoding: Always picks the highest probability token. Produces consistent but less diverse and sometimes repetitive text.
- Sampling (Top-k, Nucleus): Randomly selects from top candidates, introducing variability, creativity, and preventing repetitive loops. The choice depends on application needs—factual answers vs. creative writing.
Answer:
Safety filters check generated output against content policies (detecting hate speech, sexual content, misinformation). They prevent models from producing harmful or disallowed content. Implemented as classifier layers, rule-based checks, or additional passes over generated text/images. Ensuring responsible deployment is crucial.
Answer:
Diffusion-LM extends the diffusion process to text, gradually denoising noisy tokens to form coherent text. Though less common than in images, diffusion-based text models promise better control over generation steps and might avoid pitfalls like exposure bias. They’re an active research area aiming to combine diffusion strengths with language modeling.
Answer:
- Masked Modeling (like in BERT): Masks some tokens and predicts them, learning bidirectional context. Good for representation learning.
- Autoregressive (like GPT): Predicts the next token given the previous tokens, capturing causal patterns. Good for direct generation.
Choosing depends on whether the goal is strong understanding (masked) or generation (autoregressive).
Answer:
Few-shot prompting includes a few training examples directly in the input prompt before asking the model to perform the task on a new query. The model infers the task pattern from these examples and applies it. This approach leverages the model’s pretrained knowledge to adapt to new tasks without explicit fine-tuning.
Answer:
Chain-of-Thought prompting encourages language models to “think aloud” by giving them reasoning steps before final answers. By writing out intermediate thoughts, the model can produce more accurate, logically consistent answers. It transforms black-box predictions into more interpretable, stepwise solutions.
Answer:
Different LLMs vary in architecture details, training corpora, scale, and optimization techniques. GPT-4, PaLM, LLaMA all share transformer backbones but differ in tokenization, context lengths, capabilities (math reasoning, coding), and licensing. Some are proprietary (GPT-4), while others are open-source (LLaMA), affecting accessibility and customization.
Answer:
Image captioning models use a visual encoder (CNN or ViT) to extract features and a decoder (RNN or Transformer) to generate descriptive sentences. Attention links image regions to words. Training pairs images with reference captions. Pretrained vision-language models now achieve state-of-the-art captioning performance.
Answer:
RAG retrieves relevant documents from a large corpus and conditions a generative model on these documents. It improves factual correctness and reduces hallucinations, enabling dynamic, knowledge-grounded generation. For instance, a Q&A system that consults a database of facts before answering.
Answer:
DALL·E uses text prompts to guide an autoregressive or diffusion-based image generator. It encodes text into embeddings that condition the image generation pipeline. By learning joint text-image representations, it can produce coherent images from textual descriptions, bridging vision-language gaps.
Answer:
Inpainting fills missing or masked regions of an image with plausible content. Diffusion models or GANs learn to predict the hidden region consistent with surrounding pixels. Useful for restoring damaged photos, removing unwanted objects, or editing images interactively.
Answer:
Text-to-video models extend image generation concepts to temporal sequences. They generate frames coherent with both text prompts and temporal consistency. By conditioning on textual descriptions and modeling motion with temporal layers, these models produce short videos depicting scenes described in text.
Answer:
Hypernetworks or adapters learn small sets of parameters attached to pretrained models, enabling quick adaptation to new styles or domains without retraining the entire model. They’re efficient for personalization (e.g., style adaptation in Stable Diffusion) with fewer resources and faster turnaround.
Answer:
RLHF fine-tunes a language model using human preference data. Humans rank outputs, and a reward model predicts these preferences. The LLM is then trained with reinforcement learning (e.g., PPO) to produce responses aligning with human values and instructions. RLHF improves helpfulness, safety, and user satisfaction in chatbots.
Answer:
- Safety Filters: Post-processing or additional classifiers that block disallowed outputs.
- Adversarial Training: Training the model to resist adversarial prompts or manipulations.
Filters handle output after generation, while adversarial training modifies model parameters to be robust from the inside.
Answer:
Alignment ensures model outputs reflect desired values, ethics, or instructions. It involves techniques like RLHF, careful prompt design, or safety guardrails to align models with human intentions, reducing harmful or misleading content.
Answer:
- Zero-Shot: Model performs a task without examples, relying on general capabilities.
- Instruction-based: The prompt explicitly states instructions (e.g., “Explain in simple terms...”).
Instruction-based prompting helps models understand what’s expected, often improving performance and compliance with requests.
Answer:
A diffusion-based text-to-image model translates text prompts into latent representations, then iteratively refines noise into an image. Stable Diffusion uses a latent diffusion process, drastically reducing computational overhead and producing high-quality images from text descriptions.
Answer:
Conditioning incorporates additional signals (text prompts, class labels, sketches) influencing the generative process. It ensures outputs align with user specifications, enabling tasks like conditional image generation, style transfer, or voice cloning where users dictate desired output characteristics.
Answer:
Layout-based conditioning uses spatial layouts or bounding boxes to guide image generation. For example, you specify where objects appear, and the model fills in details. This helps produce consistent scenes with desired object arrangements, valuable in design and simulation applications.
Answer:
A tokenizer converts text into tokens (ids) for the LLM. The model operates on these tokens, predicting next tokens to generate. A well-designed tokenizer handles rare words (subwords), reduces OOV issues, and affects model performance, memory usage, and generation quality.
Answer:
Quantization reduces model weight precision (e.g., from FP32 to INT8), cutting memory and computational requirements. For generative models, careful quantization maintains output quality while enabling real-time generation on mobile or embedded devices. It’s crucial for on-device speech synthesis, AR filters, or text generation.
Answer:
Style mixing takes latent codes from different sources and injects them at various layers, merging features from multiple latent vectors. This allows for blending attributes from different faces (e.g., hair style from one face and facial features from another), enabling fine-grained and creative image editing.
Answer:
Knowledge distillation transfers a large LLM’s capabilities to a smaller model by training the smaller model to match the larger’s output distributions. This yields compact, efficient models that retain much of the original’s performance. Distilled LLMs are suitable for resource-constrained deployments without major accuracy drops.
Answer:
Instruct Models are fine-tuned to follow instructions and user prompts more reliably. They have seen instruction-response pairs and learned to produce helpful, aligned outputs. Base LLMs may be knowledgeable but less cooperative or consistent in following complex instructions, while instruct models are tailored for compliance and clarity.
Answer:
Retrieval-Enhanced models augment LLMs with a retrieval mechanism that fetches relevant text chunks from a database. Conditioning on retrieved documents improves factual accuracy and reduces hallucinations. RETRO and other retrieval-augmented setups combine large language models with external knowledge, bridging the gap between pure generative capabilities and factual reliability.
Answer:
These models combine vision encoders (like CLIP) and language decoders (LLMs) in a unified framework. They process both images and text, enabling tasks like image captioning, VQA, or grounded text generation. Cross-attention or joint embeddings fuse modalities so the model can generate text describing images or even use images as context for richer language responses.
Answer:
For diffusion or GAN models, gradient-based editing modifies latent codes using gradient signals to achieve desired changes (e.g., making a face older). Instead of random guesswork, we directly optimize latent variables to transform generated outputs, ensuring controlled semantic changes.
Answer:
- Classifier Guidance: Uses a pretrained classifier’s gradient to push samples towards a desired class, requiring an external classifier model.
- Classifier-Free Guidance: Conditions the diffusion model on class embeddings. By interpolating between conditional and unconditional scores, it guides generation without a separate classifier, offering flexibility and reducing complexity.
Answer:
LLMs trained on code corpora can generate code snippets, solve programming tasks, or assist in software development (e.g., GitHub Copilot). They learn syntax and semantics of programming languages, applying generative capabilities to produce functional code, refactor, or suggest fixes.
79. Explain the concept of BERT-like Masked Prediction vs. GPT-like Next Token Prediction in generative contexts.
Answer:
- BERT-like (Masked): Learns bidirectional representations by predicting masked tokens, good for understanding and encoding text, not inherently generative.
- GPT-like (Next token): Predicts next tokens autoregressively, naturally suited for generation. GPT-style models excel at producing fluent text.
80. How do you handle evaluation of generative models that create creative outputs (e.g., art, poetry)?
Answer:
Purely quantitative metrics (like BLEU) are insufficient. Human evaluation remains crucial—assessing creativity, coherence, style, and aesthetic appeal. Sometimes user studies, preference tests, or rating scales complement automatic metrics. Embedding-based semantic similarity metrics and adversarial Turing tests are also explored.
Answer:
Anti-aliasing prevents moiré patterns or distorted textures in generated images. Applying filters or careful upsampling methods ensures that fine details appear smooth and realistic. This attention to low-level details improves the perceptual quality of synthesized images.
Answer:
Text-based editing uses diffusion models conditioned on prompts plus existing images. By specifying a prompt that describes desired changes, the model edits the input image accordingly. For example, “Add a hat to the person” leads to a new image with the subject wearing a hat, providing powerful and user-friendly image manipulation.
Answer:
Curriculum learning starts training with simpler tasks or distributions and gradually introduces complexity. For image generators, it might begin with low-resolution images and then increment resolution. This stepwise approach can stabilize training, help models converge to better solutions, and improve output quality.
Answer:
Dropping low-probability tokens from the generation candidate set (e.g., top-k filtering or nucleus sampling) eliminates extremely unlikely outputs. It reduces the risk of nonsensical or off-topic words and can speed up inference by focusing on plausible continuations, making generation more stable and coherent.
Answer:
FaceNet maps faces into an embedding space where same-person images cluster closely, and different-person images are far apart. By optimizing a triplet loss, it directly encodes identity similarity rather than performing class-based classification. This flexible metric learning approach is central to face verification and clustering.
Answer:
GLIDE and Imagen are diffusion-based text-to-image generators that combine large language understanding (from LLM-like embeddings) with powerful generative diffusion techniques. They produce highly detailed, contextually correct images from textual descriptions. Their architecture leverages transformer text encoders and image diffusion decoders, pushing the frontier of controllable image synthesis.
Answer:
Ensembling multiple generative models (e.g., different checkpoints or architectures) can improve sample diversity, stability, and quality. By merging latent samples or blending output distributions, ensembles compensate for individual model weaknesses, resulting in more robust and appealing generated content.
88. How do Large Language Models assist in multimodal generation (e.g., text-based image editing prompts)?
Answer:
LLMs parse and interpret user instructions, generate refined textual descriptions, or specify constraints. When combined with vision models or bridging components, LLMs can produce prompts guiding image generators, enabling iterative editing, stylization, or scene adjustments via natural language instructions.
Answer:
MoE architectures split complex distributions into sub-distributions handled by different expert models. A gating network assigns weights to each expert for a given input. This can improve scalability and specialization (e.g., one expert for faces, another for landscapes). MoE reduces mode collapse and diversifies outputs by letting experts handle different data modes.
Answer:
- Greedy: Fast but can produce repetitive or suboptimal code snippets.
- Beam: Explores multiple partial solutions, potentially finding better solutions but costly.
- Sampling (top-k, nucleus): Introduces creativity and variety, useful for generating multiple candidate solutions for code suggestions or refactoring.
Answer:
- Leverage multilingual pretraining or cross-lingual transfer.
- Use data augmentation or synthetic data generation.
- Fine-tune with domain-specific unlabeled data via self-supervised tasks.
- Apply few-shot or zero-shot prompting for tasks that have limited direct examples, relying on general pretrained knowledge.
Answer:
Generative models trained on large-scale data inherit biases, stereotypes, or harmful content from their training sets. They may produce biased, offensive, or unbalanced outputs. Addressing bias involves careful dataset curation, filtering, prompt design, and bias mitigation techniques (fine-tuning, RLHF, fairness constraints).
Answer:
Latent codes compactly represent data variations. Interpolating between two latent codes blends features from both, generating intermediate samples that smoothly transition between styles or identities. This lets users morph objects, combine attributes, or explore the learned space for creative editing.
Answer:
EBMs assign an energy score to configurations, where lower energy indicates more likely data states. Sampling from EBMs involves MCMC or gradient-based methods. Though less common than GANs or VAEs, EBMs can model complex distributions and unify discriminative and generative modeling under one energy framework.
Answer:
Batch generation involves parallelizing computations across multiple samples. With efficient GPU usage, running model inference on multiple inputs at once reduces latency and increases throughput. Ensuring memory constraints and using quantization or model distillation can further speed up batch inference.
Answer:
In product design, users need to generate variations of a concept by specifying attributes (color, material). Controllable generation lets them prompt the model to produce specific styles, ensuring outputs match design requirements. Similarly, in medical imaging synthesis, controlling pathology presence helps simulate training data for AI diagnostics.
Answer:
ARDM combines autoregressive and diffusion ideas, factorizing data distribution in a sequence manner while incorporating noise removal steps. Though less common than pure diffusion or autoregressive models, it’s an experimental approach aiming to exploit both paradigms’ strengths.
Answer:
Progressive Growing starts training on low-resolution images and incrementally increases resolution by adding layers to the generator and discriminator. This stepwise approach stabilizes training, allowing models to first learn coarse features before mastering fine details. It led to breakthroughs in high-resolution image synthesis.
Answer:
A validation set guides hyperparameter selection, model architecture decisions, and early stopping. While generative tasks are subjective, validation metrics (IS, FID, perplexity) and human evaluation on validation sets ensure the chosen model generalizes and doesn’t overfit the training distribution.
Answer:
Key trends:
- Large multimodal generative models (e.g., text-to-image/video) with controllable generation.
- Diffusion models surpassing GANs in image quality and stability.
- LLMs integrated with retrieval and instruction-following, producing more truthful and user-aligned outputs.
- Tools like RLHF, watermarking, and content filters address ethical, safety, and IP concerns.
- Rapid advances in model compression, efficient sampling, and real-time generation empower broader deployment.
These 100 questions and answers provide a comprehensive overview of Generative AI, from fundamental generative modeling concepts (GANs, VAEs, autoregressive methods) to advanced diffusion models, large language models, multimodal generation, and the corresponding ethical and practical deployment considerations.