AnAppleCore
Ph.D. student @ THU | Interested in Bio-inspired AI and Continual Learning
IDG/McGovern Institute, Tsinghua UniversityBeijing, China
Pinned Repositories
AdaMix
This is the implementation of the paper AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning (https://arxiv.org/abs/2205.12410).
AdaptFormer
[NeurIPS 2022] Implementation of "AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition"
AML
AnAppleCore.github.io
Hongwei Yan's GitHub homepage.
AVION
Code release for "Training a Large Video Model on a Single Machine in a Day"
Awesome-Incremental-Learning
Awesome Incremental Learning
Bioinformatics
Course Project Repository for Bioinformatics
Blender_Camera
Create (image, camera pos) pairs with bpy API
MOSE
Multi-level Online Sequential Experts (MOSE) for online continual learning problem. (CVPR2024)
Ripple-Video-Prediction
Ripple Video Prediction using trivial convolution encoder and decoder
AnAppleCore's Repositories
AnAppleCore/MOSE
Multi-level Online Sequential Experts (MOSE) for online continual learning problem. (CVPR2024)
AnAppleCore/Bioinformatics
Course Project Repository for Bioinformatics
AnAppleCore/AdaptFormer
[NeurIPS 2022] Implementation of "AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition"
AnAppleCore/AML
AnAppleCore/AnAppleCore.github.io
Hongwei Yan's GitHub homepage.
AnAppleCore/AVION
Code release for "Training a Large Video Model on a Single Machine in a Day"
AnAppleCore/Awesome-Incremental-Learning
Awesome Incremental Learning
AnAppleCore/BAN
Unofficial pytorch implementation of Born-Again Neural Networks.
AnAppleCore/BLRNN
Bilinear RNN for Reinformcement Learning (Atari game) based on cleanrl
AnAppleCore/CBA
This is an official PyTorch implementation of CBA: Improving Online Continual Learning via Continual Bias Adaptor.
AnAppleCore/Co2L
Co^2L: Contrastive Continual Learning (ICCV2021)
AnAppleCore/DeepCAD-RT
DeepCAD-RT: Real-time denoising of fluorescence time-lapse imaging using deep self-supervised learning
AnAppleCore/DEVIAS
[ECCV 2024 Oral] Official implementation of the paper "DEVIAS: Learning Disentangled Video Representations of Action and Scene"
AnAppleCore/GSA
Official implementation of the CVPR2023 paper 'Cross-Task Class Discrimination in Online Continual Learning'
AnAppleCore/Knowledge-Distillation-Zoo
Pytorch implementation of various Knowledge Distillation (KD) methods.
AnAppleCore/logit-standardization-KD
[CVPR 2024 Highlight] Logit Standardization in Knowledge Distillation
AnAppleCore/mammoth
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
AnAppleCore/mindeye
fMRI-to-image reconstruction on the NSD dataset.
AnAppleCore/MixLoRA
State-of-the-art Parameter-Efficient MoE Fine-tuning Method
AnAppleCore/mixture-of-experts
PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538
AnAppleCore/MoE-Adapters4CL
Code for paper "Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters" CVPR2024
AnAppleCore/MoE-PEFT
An Efficient LLM Fine-Tuning Factory Optimized for MoE PEFT
AnAppleCore/OnPro
Online Prototype Learning for Online Continual Learning (ICCV 2023)
AnAppleCore/RepAdapter
Official implementation of "Towards Efficient Visual Adaption via Structural Re-parameterization".
AnAppleCore/SRDTrans
SRDTrans: Spatial redundancy transformer for self-supervised fluorescence image denoising
AnAppleCore/SUPPORT
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
AnAppleCore/TAKD
Using Teacher Assistants to Improve Knowledge Distillation: https://arxiv.org/pdf/1902.03393.pdf
AnAppleCore/tiny-transformers
[ECCV 2022] Implementation of the paper "Locality Guidance for Improving Vision Transformers on Tiny Datasets"
AnAppleCore/VideoMAEv2
[CVPR 2023] VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
AnAppleCore/VideoMamba
[ECCV2024] VideoMamba: State Space Model for Efficient Video Understanding