taeckyung / SoTTALinks
This is the official PyTorch Implementation of "SoTTA: Robust Test-Time Adaptation on Noisy Data Streams (NeurIPS '23)" by Taesik Gong*, Yewon Kim*, Taeckyung Lee*, Sorn Chottananurak, and Sung-Ju Lee (* Equal contribution).
☆22Updated last year
Alternatives and similar repositories for SoTTA
Users that are interested in SoTTA are comparing it to the libraries listed below
Sorting:
- [ICML 2023] On Pitfalls of Test-Time Adaptation☆121Updated last year
- 🔥 🔥 [ECCV 2024 Oral ] Official code for "Weighted Ensemble Models Are Strong Continual Learners"☆29Updated 5 months ago
- Code for ICML 2022 paper — Efficient Test-Time Model Adaptation without Forgetting☆128Updated 2 years ago
- Code for "BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation [ICML2024]".☆43Updated last year
- The official PyTorch Implementation of "NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation (NeurIPS '22)"☆48Updated last year
- ☆42Updated 2 years ago
- ☆67Updated 2 years ago
- Code for ICML 2024 paper (Oral) — Test-Time Model Adaptation with Only Forward Passes☆87Updated last year
- [TMLR'24] This repository includes the official implementation our paper "Unleashing the Power of Visual Prompting At the Pixel Level"☆41Updated last year
- Official implementation for CVPR'23 paper "BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning"☆110Updated 2 years ago
- This is a community implementation for the paper EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularizatio…☆34Updated 2 years ago
- Code for ICLR 2023 paper (Oral) — Towards Stable Test-Time Adaptation in Dynamic Wild World☆191Updated 2 years ago
- Official Implementation of CVPR 2022 paper: "Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning…☆34Updated 2 years ago
- This codebase is the official implementation of Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (NeurIPS2…☆101Updated 3 years ago
- [CVPR 2024] Official Repository for "Efficient Test-Time Adaptation of Vision-Language Models"☆105Updated last year
- Test-Time Adaptation via Conjugate Pseudo-Labels☆42Updated 2 years ago
- ☆62Updated last year
- ☆23Updated last year
- PyTorch implementation of our CVPR 2024 paper "Unified Entropy Optimization for Open-Set Test-Time Adaptation"☆27Updated last year
- [CVPR23] "Understanding and Improving Visual Prompting: A Label-Mapping Perspective" by Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zha…☆53Updated 2 years ago
- Code for Continuously Changing Corruptions (CCC) benchmark + evaluation☆37Updated last year
- Test-time adaptation via Nearest neighbor information (TAST), submitted to ICLR'23☆23Updated 2 years ago
- Official implementation of "Towards Distribution-Agnostic Generalized Category Discovery" (NIPS 2023)☆25Updated last year
- [NeurIPS] TTT++: When Does Self-supervised Test-time Training Fail or Thrive?☆70Updated 3 years ago
- source code for ICLR'23 paper "Non-parametric Outlier Synthesis"☆52Updated last year
- [CVPR'24] Validation-free few-shot adaptation of CLIP, using a well-initialized Linear Probe (ZSLP) and class-adaptive constraints (CLAP)…☆76Updated 3 months ago
- ActMAD: Activation Matching to Align Distributions for Test-Time-Training (CVPR 2023)☆20Updated 2 years ago
- Exploring prompt tuning with pseudolabels for multiple modalities, learning settings, and training strategies.☆50Updated 10 months ago
- Towards Unified and Effective Domain Generalization☆31Updated last year
- ☆41Updated 10 months ago