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acol

Adversarial Complementary Learning for Weakly Supervised Object Localization

awesome-domain-adaptation

A collection of AWESOME things about domian adaptation

aws-sagemaker-pytorch-shop-by-style

A Personalized 'Shop-by-Style' Experience via PyTorch on Amazon SageMaker and Amazon Neptune

bilinear-cnn

PyTorch implementation of bilinear CNN for fine-grained image recognition

blinear-cnn-faster

A Faster PyTorch implementation of bilinear CNN for fine-grained image recognition

confusion

Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification"

cse508_class_project

d2l-pytorch

This is an attempt to modify Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook's code into PyTorch.

facial-similarity-with-siamese-networks-in-pytorch

Implementing Siamese networks with a contrastive loss for similarity learning

learntopayattention

PyTorch implementation of ICLR 2018 paper Learn To Pay Attention (and some modification)

lfw_siamese_pytorch

This project uses the ​Labeled Faces in the Wild​ (LFW) dataset​, and the goal is to train variants of deep architectures to learn when a pair of images of faces is the same ​person​ or not. It is a pytorch implementation of Siamese network with 19 layers.

math51

Collection of my assignments and work in the class MATH51 at Stanford

misc

Medical Images misc

mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

multi-level-vae

This repository contains a pytorch implementation for the paper: Multi-Level Variational Autoencoder (https://arxiv.org/abs/1705.08841), which was accepted at AAAI-18.

nuclei-segmentation

PyTorch implementation of U-Net image segmentation for Kaggle Data Science Bowl 2018 competition

prantikhowlader.github.io

pytorch_09_20_22

add openslide

quip_cnn_segmentation

CNN based segmentation codes

self-attention-gan

simclr

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by T. Chen et al.

ssn_superpixels

Superpixel Sampling Networks (ECCV2018)

vu