Pinned Repositories
align_uniform
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere.
ANCOR
Fine-grained Angular Contrastive Learning with Coarse Labels - Official Repository
bayesian-neural-network-blogpost
Building a Bayesian deep learning classifier
BEVT
PyTorch implementation of BEVT (CVPR 2022) https://arxiv.org/abs/2112.01529
cdfsl-benchmark
(ECCV 2020) Cross-Domain Few-Shot Learning Benchmarking System
CFA
Class-Incremental Learning via Knowledge Amalgamation - ECML PKDD 2022
CommonFeatureLearning
(IJCAI 2019) Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning
ConfidNet
Addressing Failure Prediction by Learning Model Confidence
External-Attention-pytorch
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
PyCIL
PyCIL: A Python Toolbox for Class-Incremental Learning
sender666's Repositories
sender666/External-Attention-pytorch
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
sender666/PyCIL
PyCIL: A Python Toolbox for Class-Incremental Learning
sender666/align_uniform
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere.
sender666/BEVT
PyTorch implementation of BEVT (CVPR 2022) https://arxiv.org/abs/2112.01529
sender666/CFA
Class-Incremental Learning via Knowledge Amalgamation - ECML PKDD 2022
sender666/ConfidNet
Addressing Failure Prediction by Learning Model Confidence
sender666/Contrastive-Clustering
Code for the paper "Contrastive Clustering" (AAAI 2021)
sender666/deepmind-research
This repository contains implementations and illustrative code to accompany DeepMind publications
sender666/domain_specific_cl
Code for NeurIPS 2020 article "Contrastive learning of global and local features for medical image segmentation with limited annotations"
sender666/GroupMixFormer
GroupMixAttention and GroupMixFormer
sender666/GWTune
sender666/HowToLiveLonger
程序员延寿指南 | A programmer's guide to live longer
sender666/Image_Forgery_Detection
Reproduced Code for Image Forgery Detection papers.
sender666/KDEP
(CVPR2022) Official PyTorch Implementation of KDEP. Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability
sender666/mdistiller
A Knowledge Distillation Toolbox. The official implementation of https://arxiv.org/abs/2203.08679
sender666/MedicalZooPytorch
A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation
sender666/Mini-batch-OT
A new mini-batch framework for optimal transport in deep generative models, deep domain adaptation, approximate Bayesian computation, color transfer, and gradient flow.
sender666/mot
Approximative algorithms for computing sparse Wasserstein-2 multi-marginal optimal transport plans and associated free support barycenters.
sender666/PCS-FUDA
Official implementation of PCS in essay "Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation"
sender666/PiCO
PyTorch implementation of PiCO https://arxiv.org/abs/2201.08984
sender666/renet
[ICCV'21] Official PyTorch implementation of Relational Embedding for Few-Shot Classification
sender666/S2SNet
Repo for S2SNet
sender666/SimMIM
This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".
sender666/SSDA_MME
Semi-supervised Domain Adaptation via Minimax Entropy
sender666/testgit
sender666/TMC
The project page of paper: Trusted Multi-View Classification [ICLR'2021 paper]
sender666/unbalanced_gromov_wasserstein
Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport
sender666/UncertaintyInNeuralNetworks_Resources
sender666/Unsupervised-Classification
SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
sender666/vissl
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.