Egg-Hu's Stars
horseee/LLM-Pruner
[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support Llama-3/3.1, Llama-2, LLaMA, BLOOM, Vicuna, Baichuan, TinyLlama, etc.
IDEA-Research/DWPose
"Effective Whole-body Pose Estimation with Two-stages Distillation" (ICCV 2023, CV4Metaverse Workshop)
sdc17/HeuristicDropout
[ICASSP 2022] Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models.
sdc17/CrossGET
[ICML 2024] CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers.
sdc17/MEPDNet
[ICIP 2021] Multi-Encoder Parse-Decoder Network for Sequential Medical Image Segmentation.
sdc17/UPop-Project
[ICML'23] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.
sdc17/UPop
[ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.
ShipengWang/Adam-NSCL
PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"
jjbrophy47/machine_unlearning
Existing Literature about Machine Unlearning
eric-mitchell/serac
Semi-Parametric Editing with a Retrieval-Augmented Counterfactual Model
zjunlp/KnowledgeEditingPapers
Must-read Papers on Knowledge Editing for Large Language Models.
JamesQFreeman/LoRA-ViT
Low rank adaptation for Vision Transformer
gortizji/tangent_task_arithmetic
Source code of "Task arithmetic in the tangent space: Improved editing of pre-trained models".
illidanlab/ABD
[ICML2023] Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Egg-Hu/PURER-Plus
PURER-Plus: An Extension of PURER (CVPR-2023)
Egg-Hu/PURER
Official Pytorch Implementation for "Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning" (CVPR-2023)
Egg-Hu/BiDf-MKD
Official Pytorch Implementation for "Learning to Learn from APIs: Black-Box Data-Free Meta-Learning" (ICML-2023)
ttengwang/Awesome_Prompting_Papers_in_Computer_Vision
A curated list of prompt-based paper in computer vision and vision-language learning.
tristandeleu/pytorch-meta
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
sungyubkim/GBML
A collection of Gradient-Based Meta-Learning Algorithms with pytorch
Open-Debin/Bayesian_MQDA
Shallow Bayesian Meta Learning for Real World Few-shot Recognition
bethgelab/foolbox
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
RobustBench/robustbench
RobustBench: a standardized adversarial robustness benchmark [NeurIPS'21 Benchmarks and Datasets Track]
zju-vipa/CMI
[IJCAI-2021] Contrastive Model Inversion for Data-Free Knowledge Distillation
PatrickZH/DeepCore
Code for coreset selection methods
sidak/otfusion
Model Fusion via Optimal Transport, NeurIPS 2020
google-research/meta-dataset
A dataset of datasets for learning to learn from few examples
learnables/learn2learn
A PyTorch Library for Meta-learning Research
oreillymedia/t-SNE-tutorial
A tutorial on the t-SNE learning algorithm
IandRover/MAML_noisy_contrasive_learner