Alternatives and similar repositories for bias:

Users that are interested in bias are comparing it to the libraries listed below

• UCSC-VLAA / DMAE
  [CVPR 2023] This repository includes the official implementation our paper "Masked Autoencoders Enable Efficient Knowledge Distillers"
  ☆104Updated last year
• Haochen-Wang409 / DropPos
  [NeurIPS'23] DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
  ☆61Updated 10 months ago
• liuxingbin / dbot
  [ICLR2024] Exploring Target Representations for Masked Autoencoders
  ☆53Updated last year
• OliverRensu / DeepMIM
  ☆52Updated 2 years ago
• yossigandelsman / test_time_training_mae
  The official github repo for "Test-Time Training with Masked Autoencoders"
  ☆80Updated last year
• ggjy / DeLVM
  ☆114Updated 9 months ago
• UMass-Embodied-AGI / Mod-Squad
  ☆82Updated last year
• zejiangh / MILAN
  PyTorch implementation of the paper "MILAN: Masked Image Pretraining on Language Assisted Representation" https://arxiv.org/pdf/2208.0604…
  ☆82Updated 2 years ago
• Jiahao000 / MFM
  [ICLR 2023] Masked Frequency Modeling for Self-Supervised Visual Pre-Training
  ☆74Updated last year
• hunto / DiffKD
  Official implementation for paper "Knowledge Diffusion for Distillation", NeurIPS 2023
  ☆81Updated last year
• ZhangYuanhan-AI / visual_prompt_retrieval
  [NeurIPS2023] Official implementation and model release of the paper "What Makes Good Examples for Visual In-Context Learning?"
  ☆173Updated last year
• OpenDriveLab / maskalign
  [CVPR 2023] Official repository for paper "Stare at What You See: Masked Image Modeling without Reconstruction"
  ☆69Updated last year
• ziplab / SPT
  [ICCV 2023 oral] This is the official repository for our paper: ''Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning''.
  ☆65Updated last year
• Paranioar / UniPT
  [CVPR2024] The code of "UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory"
  ☆67Updated 5 months ago
• JieShibo / PETL-ViT
  [ICCV 2023 & AAAI 2023] Binary Adapters & FacT, [Tech report] Convpass
  ☆179Updated last year
• dongzelian / SSF
  [NeurIPS'22] This is an official implementation for "Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning".
  ☆177Updated last year
• eric-ai-lab / PEViT
  Official implementation of AAAI 2023 paper "Parameter-efficient Model Adaptation for Vision Transformers"
  ☆104Updated last year
• CVMI-Lab / SlotCon
  (NeurIPS 2022) Self-Supervised Visual Representation Learning with Semantic Grouping
  ☆97Updated last week
• ZhangYuanhan-AI / NOAH
  [TPAMI] Searching prompt modules for parameter-efficient transfer learning.
  ☆226Updated last year
• mihirp1998 / Diffusion-TTA
  Diffusion-TTA improves pre-trained discriminative models such as image classifiers or segmentors using pre-trained generative models.
  ☆68Updated 11 months ago
• geekyutao / TaskRes
  Task Residual for Tuning Vision-Language Models (CVPR 2023)
  ☆70Updated last year
• LightDXY / BootMAE
  ECCV2022,Bootstrapped Masked Autoencoders for Vision BERT Pretraining
  ☆97Updated 2 years ago
• lisadunlap / ALIA
  Augmenting with Language-guided Image Augmentation (ALIA)
  ☆75Updated last year
• Koorye / DePT
  [CVPR 2024] Offical implemention of the paper "DePT: Decoupled Prompt Tuning"
  ☆94Updated 3 months ago
• chenshuang-zhang / imagenet_d
  [CVPR 2024 Highlight] ImageNet-D
  ☆41Updated 5 months ago
• OpenGVLab / DiffRate
  [ICCV 23]An approach to enhance the efficiency of Vision Transformer (ViT) by concurrently employing token pruning and token merging tech…
  ☆93Updated last year
• Haochen-Wang409 / HPM
  [CVPR'23] Hard Patches Mining for Masked Image Modeling
  ☆90Updated last year
• Sense-X / TokenMix
  TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers (ECCV 2022)
  ☆93Updated 2 years ago
• ChengHan111 / E2VPT
  Official Pytorch implementation of "E2VPT: An Effective and Efficient Approach for Visual Prompt Tuning". (ICCV2023)
  ☆68Updated last year
• zhangq327 / U-MAE
  Official Code for NeurIPS 2022 Paper: How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
  ☆58Updated last year