Paper-Implementations

Implementation attempts of various AI papers in simple form etc for my learning purposes. These implementations are not meant to be exact and are categorized into a general section.

WARNING: Codes may be incomplete, will likely have bugs, mistakes. Reports for any bugs, mistakes welcome.

🚀 Represents I am fairly confident implementation works (some things may not be same as defined in paper) on custom dataset and at least part of it is close enough to proposed paper topic.

In newer codes mostly tried to follow PEP 8 and google python style guide. Old codes have poor coding conventions and quality.

Pytorch

Topic	Code
Generative Adverserial Networks (GAN)	🚀 Generative Adversarial Networks (GAN)
	🚀 Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (DCGAN)
	🚀 Wasserstein GAN (WGAN)
	🚀 Improved Training of Wasserstein GANs (WGAN-GP)
	🚀 Conditional Generative Adversarial Nets
	Semantic Image Synthesis with Spatially-Adaptive Normalization (GauGAN/SPADE)
	Progressive Growing of Gans for Improved Quality, Stability, and Variation (ProGAN)
Denoising Diffusion	🚀 Denoising Diffusion Probabilistic Models (DDPM)
	🚀 Denoising Diffusion Implicit Models (DDIM)
Multimodal	Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Imagen)
Transformers, Attention Mechanisms	🚀 Attention is all you need (Transformers, Self Attention, Multi-Head Self Attention)
Neural Style Transfer	Image Style Transfer Using Convolutional Neural Networks (NST)
Knowledge Distillation	🚀 Distilling the Knowledge in a Neural Network (Knowledge Distillation)
Vision Transformer	🚀 An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)
Image Segmentation	U-Net: Convolutional Networks for Biomedical Image Segmentation (UNet)
Verification, Recognition, Clutering TAG: Siamese Network (Siam)	🚀 FaceNet: A Unified Embedding for Face Recognition and Clustering (Triplet Loss)
	🚀 Siamese Neural Networks for One-shot Image Recognition       Dimensionality Reduction by Learning an Invariant Mapping (Contrastive Loss)
Implicit Representations	Implicit Neural Representations with Periodic Activation Functions (SIREN) COIN: COmpression with Implicit Neural representations

Tools

Various tools useful for custom training. These are not paper implementation.

Topic	Code
Image Resize, Verification	🚀 Fast full image dataset resize and corrupted, low resolution image remover
Image Captioning	🚀 Image to text caption generation
Embedding, Feature extraction	🚀 Embedding Generation, Feature Extraction from Text and Images
Image Depth Generation	Generates depth images from single or multiple image
Image Segmentation Generation	Generates semantic, instance, panoptic etc. segmentation images from an image

Papers of Interest

Papers links are available here, https://github.com/quickgrid/AI-Resources/blob/master/papers.md.