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gumbel_softmax_vae

PyTorch implementation of a Variational Autoencoder with Gumbel-Softmax Distribution

hnerv

A hybrid neural representation for videos

how-do-vits-work

(ICLR 2022 Spotlight) Official PyTorch implementation of "How Do Vision Transformers Work?"

human-pose-estimation.pytorch

The project is an official implement of our ECCV2018 paper "Simple Baselines for Human Pose Estimation and Tracking(https://arxiv.org/abs/1804.06208)"

hypertorch

iba-paper-code

Code for the Paper "Restricting the Flow: Information Bottlenecks for Attribution"

icml18-jtnn

Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

ihaeyong.github.io

imagenet-pytorch

The 'ImageNet training in PyTorch' tutorial, customized

iwae

Praktikum: Deep Learning in Real World 2017 - Importance Weighted Autoencoder

iwae-pytorch

Simple Importance Weighted Autoencoders (IWAE) implementation in Pytorch

iwae-tensorflow

Tensorflow implementation of Importance Weighted Auto Encoder

jdot

Joint distribution optimal transportation for domain adaptation

joint-gan

keras-yolo2

Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).

knowledgegraphembedding

knowledge_representation_pytorch

Several knowledge graph representation algorithms implemented with pytorch

kr-wordrank

비지도학습 방법으로 한국어 텍스트에서 단어/키워드를 자동으로 추출하는 라이브러리입니다

lds

Learning Discrete Structures for Graph Neural Networks (TensorFlow implementation)

learning-invariances

Codebase for Learning Invariances in Neural Networks

lectures

Oxford Deep NLP 2017 course

lieconv

llden

Lifelong Learning with Dynamically Expandable Networks

long-tailed-cil

long-tailed-recognition.pytorch

[NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation (LVIS). It is also a PyTorch implementation of the NeurIPS 2020 paper 'Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect'.

loss-decoupling-for-task-agnostic-continual-learning

The official implementation of the NeurIPS'2023 work Loss Decoupling for Task-Agnostic Continual Learning

ma598

machine-learning-i

Slides and lecture notes for the course 'machine learning I' taught at the Graduate School Neural Information Processing in Tuebingen.

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

map

mean Average Precision - This code evaluates the performance of your neural net for object recognition.