Semi-supervised learning via Compact Latent Space Clustering
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Updated
Jun 6, 2019 - Python
Semi-supervised learning via Compact Latent Space Clustering
TensorFlow Implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
Implements the ideas presented in https://arxiv.org/pdf/2004.11362v1.pdf by Khosla et al.
This is a repository for the paper "Contrastive Multiple Correspondence Analysis (cMCA): Applying the Contrastive Learning Method to Identify Political Subgroups."
Simple PyTorch implementation of Bootstrap Your Own Latent (BYOL).
PyTorch Implementation of the paper 'A Simple Framework for Contrastive Learning of Visual Representations' (ICML 2020)
Unofficial PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by Ting Chen et al.
Semantic Consistency driven Contrastive Multi Label Image Classification
Aim to implement SimCLRv2 (https://arxiv.org/abs/2006.10029) in the similar manner with keras-implemented Mask_RCNN (https://github.com/matterport/Mask_RCNN)
Bootstrap Your Own Latent (BYOL) in PyTorch
PyTorch Implementation for the paper "DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors" (ECCVW'20).
This repo demostrates how to use the concept of contrastive learning in an anommaly detection setting with autoencoders (also know as discriminative autoencoders)
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
A PyTorch implementation of MoCo based on CVPR 2020 paper "Momentum Contrast for Unsupervised Visual Representation Learning"
Independent implementation of Supervised Contrastive Loss. Straight to the point and beyond
Unofficial implement of CLSA(Contrastive Learning with Stronger Augmentations) with minimum modifications on official moco's code
"Contrastive Learning for Unpaired Image-to-Image Translation" in TensorFlow 2
2020 NeurIPS SAS, "Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement".
Implementation of Pixel-level Contrastive Learning, proposed in the paper "Propagate Yourself", in Pytorch
A simple PyTorch tutorial for helping one implement the SimCLR framework.
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