Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Published papers focusing on graph domain adaptation

The official GitHub page for the survey paper "A Survey of Large Language Models".

A curated (most recent) list of resources for Learning with Noisy Labels

SKD : Self-supervised Knowledge Distillation for Few-shot Learning

Domain Adaptation and Generalization for Medical Image Analysis

[IJCAI-2021&&TNNLS-2022] Official implementation of Hierarchical Self-supervised Augmented Knowledge Distillation

Official PyTorch implementation of PS-KD

Code for <Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training> in ECCV18

(ECCV 2020) Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation

repository for Universal Domain Adaptation through Self-supervision

Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”

code for our TPAMI 2021 paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

code for our CVPR 2022 paper "DINE: Domain Adaptation from Single and Multiple Black-box Predictors"

[MICCAI'21] Source-Free domain adaptive fundus image segmentation with denoised pseudo-labeling

code released for our ICML 2020 paper "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation"

Pytorch Implementation of TripLet Loss for Unsupervised Domain Adaptation

Domain Adaptation Based on the Triplet Loss

Code for Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning (NeurIPS 2022).

pytorch implementation for Contrastive Adaptation Network

Implementation of CLUDA: Contrastive learning in Unsupervised Domian Adaptation in Semantic Segmentation

impelmentation of https://arxiv.org/pdf/2001.05647.pdf

A game theoretic approach to explain the output of any machine learning model.

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Harmonization tools for multi-site neuroimaging analysis. Implemented as a python package. Harmonization of MRI, sMRI, dMRI, fMRI variables with support for NIFTI images. Complements the work in Ne…

STAGIN: Spatio-Temporal Attention Graph Isomorphism Network

Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"

LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解，记录自己的leetcode解题之路。)