Change Detection Review
A review of change detection methods, including codes and open data sets for deep learning. From paper: change detection based on artificial intelligence: state-of-the-art and challenges.
PyTorch open-source toolbox for unsupervised or domain adaptive object re-ID.
Ladder network is a deep learning algorithm that combines supervised and unsupervised learning.
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss
[CVPR 2017] Unsupervised deep learning using unlabelled videos on the web
This is the official PyTorch implementation of the CVPR 2020 paper "TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting".
Python re-implementation of the spectral clustering algorithm in the paper "Speaker Diarization with LSTM"
PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Vanilla GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to implementations of stable GAN variations (i.e. ACGan, InfoGAN) and other promising variations of GANs like conditional and Wasserstein.
Learning Depth from Monocular Videos using Direct Methods, CVPR 2018
The TensorFlow reference implementation of 'GEMSEC: Graph Embedding with Self Clustering' (ASONAM 2019).
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
A PyTorch implementation of SimCLR based on ICML 2020 paper "A Simple Framework for Contrastive Learning of Visual Representations"
Paddle Distributed Training Extended. 飞桨分布式训练扩展包
The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at <http://www.cs.cmu.edu/~aayushb/pixelNet/>.
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A stable algorithm for GAN training
Supplementary material for Hands-On Machine Learning with R, an applied book covering the fundamentals of machine learning with R.
Pytorch implementation of "One-Sided Unsupervised Domain Mapping" NIPS 2017
Hidden Two Stream
Caffe implementation for "Hidden Two-Stream Convolutional Networks for Action Recognition"
A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)
Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).
A Compositional Object-Based Approach to Learning Physical Dynamics
R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.
Code release for "Canonical Surface Mapping via Geometric Cycle Consistency"
t-Distributed Stochastic Neighbor Embedding (t-SNE) in Go
A scalable implementation of "Learning Structural Node Embeddings Via Diffusion Wavelets (KDD 2018)".
Codes for Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement (EMNLP 2018)
Pytorch code for our ICLR 2017 paper "Layered-Recursive GAN for image generation"
Flappy Bird AI using Evolution Strategies
A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. In CVPR, 2017.
Pytorch implementation of "One-Shot Unsupervised Cross Domain Translation" NIPS 2018
The official PyTorch implementation of the paper "Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation".
Official Pytorch Implementation for ICML'19 paper: Unsupervised Deep Learning by Neighbourhood Discovery
e3d-lstm; Eidetic 3D LSTM A Model for Video Prediction and Beyond
[CVPR19] DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency (Oral paper)
Training and evaluating a variational autoencoder for pan-cancer gene expression data
Gradient Origin Networks - a new type of generative model that is able to quickly learn a latent representation without an encoder
The authors' implementation of Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations
The standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.