A state-of-the-art semi-supervised method for image recognition

Label-Efficient Semantic Segmentation with Diffusion Models (ICLR'2022)

Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

Code for CVPR 2019 paper Label Propagation for Deep Semi-supervised Learning

Faster Learning on Anchor Graph

scalable active learning by approximated error reduction

Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization

Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

The HSIC Bottleneck: Deep Learning without Back-Propagation

《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版

ICAE code(An Image Clustering Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids and MMD Distance)

Hilbert-Schmidt Independence Criterion

Popular TL Datasets

Implementation of Adversarial Domain Adaptation with Domain Mixup (AAAI 2020 Oral).

Code for the paper "Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation" (AAAI-2019)

Code for Learning Bregman Divergences

Code for discriminative joint probability MMD (DJP-MMD)

Methods and Implements of Deep Clustering

Importance Filtered Cross-Domain Adaptation

Adaptive Local Neighbors for Transfer Discriminative Feature Learning

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

Code for "Transfer Independently Together: A Generalized Framework for Domain Adaptation" published on IEEE TCYB

Unsupervised Domain Adaptation Papers and Code

Sparsely-Labeled Source Assisted Domain Adaptation

Maximum Density Divergence for Domain Adaptation, TPAMI 2020, Code release, Cross-domain Adversarial Tight Match

Alleviating Feature Confusion for Generative Zero-shot Learning, ACM MM 2019