OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.

Code for paper titled "Towards the Law of Capacity Gap in Distilling Language Models"

An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.

[ACL 2024] MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues

neuroCombat R package

An unofficial PyTorch implementation of VoxelMorph- An unsupervised 3D deformable image registration method

Code for Recurrent Mask Refinement for Few-Shot Medical Image Segmentation (ICCV 2021).

PyTorch implementation of 'Squeeze and Excite' Guided Few Shot Segmentation of Volumetric Scans

Code for ICLR2021 paper "Robust and Generalizable Visual Representation Learning via Random Convolutions"

[IEEE-TMI'22] Causality-inspired Single-source Domain Generalization for Medical Image Segmentation (code&data-processing pipeline)

Certifying Some Distributional Robustness with Principled Adversarial Training (https://arxiv.org/abs/1710.10571)

LaTeX template for BIT thesis

Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

Official PyTorch implementation of SegFormer

Implementations of recent research prototypes/demonstrations using MONAI.

White matter tractography clustering and more...

Multi-platform, free open source software for visualization and image computing.

Diffusion MRI analysis and visualization in 3D Slicer open source medical imaging platform.

Xuhong Li, Yves Grandvalet, and Franck Davoine. "Explicit Inductive Bias for Transfer Learning with Convolutional Networks." In ICML 2018.

L2-SP regularization minimal example. Reference: "Explicit Inductive Bias for Transfer Learning with Convolutional Networks."

DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks https://arxiv.org/abs/1901.09229

Implementation codes and datasets used in ICLR'22 Spotlight paper AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning.

Learning What and Where to Transfer (ICML 2019)

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

Budget-Aware Adapters for Multi-Domain Learning (ICCV 2019)