soumith/ganhacks

Features

• Generative Adversarial Networks - Implements and stabilizes GAN architectures to generate synthetic data while preventing mode collapse.
• Adversarial Loss Functions - Adjusts objective functions and implements label flipping to prevent vanishing gradients during generator training.
• Sampling - Implements Gaussian distributions and spherical interpolations for sampling latent space inputs.
• Conditional Training - Integrates discrete labels and auxiliary classification into the generative adversarial training process.
• Conditional Image Generation - Uses discrete labels and embeddings to guide a generative model toward producing specific categories of synthetic imagery.
• Model Implementation Templates - Provides a starter framework for building GANs based on researched training heuristics and architectural patterns.
• Adversarial Training Procedures - Implements competitive training loops for optimizing generator and discriminator networks in adversarial architectures.
• Adversarial Training Starters - Provides a collection of techniques and layers designed to stabilize training and prevent mode collapse.
• PyTorch Training Frameworks - Implements Gaussian latent sampling and normalization layers within a PyTorch-based generative framework.
• Generative Mode Collapse Prevention - Implements architectural and training heuristics to prevent vanishing gradients and output repetition.
• Synthetic Data Generators - Creates high-quality artificial samples through latent space sampling and discriminator feedback.
• Training Stability Techniques - Applies normalization and noise injection to prevent vanishing gradients and oscillations during competitive training.
• Conditional Embedding Layers - Utilizes embedding layers and additional image channels to support guided generation using discrete labels.
• Batch Normalization - Implements normalization using separate mini-batches for real and fake data to stabilize learning.
• Conditional Embedding Channels - Converts discrete labels into image channels to guide the generative process toward specific target classes.
• Auxiliary Classification Heads - Provides a classification head to the discriminator to improve training signals using ground truth labels.
• Persistent Noise Dropout - Implements high-rate dropout during both training and inference to provide continuous noise to the generator.
• Experience Replay Buffers - Implements memory structures that store historical generated samples to stabilize training and prevent catastrophic forgetting.
• Normalization Layers - Implements batch and instance normalization layers to center and scale activations for improved convergence.
• Gradient Flow Stabilizers - Uses leaky activations and avoids max-pooling to maintain stable gradient flow across deep network layers.
• Hyperparameter Tuning - Optimizes loss functions and optimizer pairings to improve convergence speed and training health.
• Hybrid Loss and Architecture Integration - Enables merging different model types or loss functions to stabilize training when standard architectures fail.
• Divergent Optimizer Pairings - Pairs different optimization algorithms for the generator and discriminator to balance the speed of convergence.
• Stochastic Noise Injection - Adds decaying Gaussian noise to inputs and layers to stabilize the adversarial learning process.
• Training Convergence Optimization - Accelerates training convergence by pairing adaptive ADAM optimizers for the generator and SGD for the discriminator.
• Simultaneous Authenticity and Class Labeling - Allows training a discriminator to perform simultaneous classification and authenticity detection.
• Label Smoothing Utilities - Provides utilities to adjust target labels with random noise to prevent discriminator overconfidence.
• Generative Adversarial Networks - Practical tips and tricks for training stable generative models.
• Educational Resources - Collection of tips and tricks for training GANs.

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