Semi-supervised SimCLR for TTT++
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Updated
Feb 14, 2022 - Python
Semi-supervised SimCLR for TTT++
[NeurIPS21] TTT++: When Does Self-supervised Test-time Training Fail or Thrive?
Code base for "On-the-Fly Test-time Adaptation for Medical Image Segmentation"
Test-time adaptation for speech recognition model by single utterance. The official implementation of "Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition" paper.
[ECCV 2022] Learning Instance-Specific Adaptation for Cross-Domain Segmentation
[MICCAI'22] Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift
Code for experiments of the paper "A new benchmark for group distribution shifts in hand grasp regression for object manipulation. Can meta-learning raise the bar?" (DistShift workshop, NeurIPS 2022).
[https://arxiv.org/abs/2208.09198] Test-Time Training for Universal Cross-Domain Retrieval
Slot-TTA shows that test-time adaptation using slot-centric models can improve image segmentation on out-of-distribution examples.
[ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"
[ICLR 2023] Test-time Robust Personalization for Federated Learning
[ICCV 2023] This repo is official PyTorch implementation of Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction.
[ICML 2023] Official code for our paper: 'Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models'
[NeurIPS 2022] Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
[NeurIPS 2023] “SODA: Robust Training of Test-Time Data Adaptors”
Towards Unified and Effective Domain Generalization
The official PyTorch Implementation of "NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation (NeurIPS '22)"
[NeurIPS 2023] Adaptive Test-Time Personalization for Federated Learning. Wenxuan Bao, Tianxin Wei, Haohan Wang, Jingrui He.
Official repository for AAAI2024 paper <Unraveling Batch Normalization for Realistic Test-Time Adaptation>.
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