PyTorch Implement of Context Encoders: Feature Learning by Inpainting

PyTorch implementation of [Context Encoders: Feature Learning by Inpainting

Implement VAE & Context-encoder using PyTorch.

PyTorch implementation of an encoder-decoder network that can recolor images based on context

A PyTorch implementation of the Hierarchical Recurrent Encoder-Decoder model, with an added layer of attention over context states

fork of https://github.com/BoyuanJiang/context_encoder_pytorch, updated for python3 and modified

context-encoder on pytorch

Modified context encoder PyTorch implementation

the pytorch implementation of context_encoder

SLAM v1 is a transformer-based model combining multi-head self-attention and Encoder Fusion (EF) cycles to optimize efficiency and context processing. It captures global context, improves memory usage, and enhances performance for NLP tasks like text classification. Implemented in PyTorch, SLAM v1 is customizable for various datasets.

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Medical image artifact removal using Generative AI

Implement context encoder with PyTorch

Context Encoder for Image Inpainting using Pytorch

ContextEncoder-Replication is a PyTorch implementation of Context Encoders, learning image features via inpainting. It includes encoder-decoder, discriminator, masking, and all loss functions in a clean, modular setup.

This project implements a Transformer-based Urdu Conversational Chatbot built from scratch using PyTorch, without any pre-trained models. The chatbot is designed to generate fluent, context-aware Urdu responses using Encoder–Decoder architecture with Multi-Head Attention and Positional Encoding.