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Test-time Adaptation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/michaelliyunhao/skeleton2humanoid
공식 코드Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening
https://github.com/mihirp1998/Slot-TTA
공식 코드https://github.com/mingukjang/tast
공식 코드Test-Time Adaptation via Self-Training with Nearest Neighbor Information
https://github.com/mkashifali/metavideostab
공식 코드Harnessing Meta-Learning for Improving Full-Frame Video Stabilization
https://github.com/ml4ai/mliis
공식 코드https://github.com/motasemalfarra/online_test_time_adaptation
공식 코드Evaluation of Test-Time Adaptation Under Computational Time Constraints
https://github.com/mr-eggplant/eata
공식 코드https://github.com/mr-eggplant/foa
공식 코드https://github.com/mr-eggplant/sar
공식 코드https://github.com/multimodallearning/dg-tta
공식 코드https://github.com/myungsub/meta-interpolation
공식 코드https://github.com/mzhaoshuai/rlcf
공식 코드Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models
https://github.com/n3il666/meta-dmoe
공식 코드Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
https://github.com/namkoong-lab/llm-tabular-shifts
공식 코드LLM Embeddings Improve Test-time Adaptation to Tabular $Y|X$-Shifts
https://github.com/neerakara/test-time-adaptable-neural-networks-for-domain-generalization
공식 코드Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
https://github.com/neerakara/tta-1d-density-matching
공식 코드A Field of Experts Prior for Adapting Neural Networks at Test Time
https://github.com/nneurotransmitterr/tm-otta-snn
공식 코드Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks
https://github.com/nyquixt/cascadetta
공식 코드Adaptive Cascading Network for Continual Test-Time Adaptation
https://github.com/oceanypt/coldqa-tta
공식 코드Robust Question Answering against Distribution Shifts with Test-Time Adaptation: An Empirical Study
https://github.com/oceanypt/multi-source-tta
공식 코드Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering
https://github.com/oripress/ccc
공식 코드RDumb: A simple approach that questions our progress in continual test-time adaptation
https://github.com/oyt9306/decorruptor
공식 코드Efficient Diffusion-Driven Corruption Editor for Test-Time Adaptation
https://github.com/pascalschlachter/comet
공식 코드COMET: Contrastive Mean Teacher for Online Source-Free Universal Domain Adaptation
https://github.com/pengxin-guo/adcsd
공식 코드Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting
https://github.com/pilshan/rem
공식 코드Ranked Entropy Minimization for Continual Test-Time Adaptation
https://github.com/po-sheng/meta_transferring_for_deblurring
공식 코드https://github.com/pythonot/convolutional-monge-mapping-normalization
공식 코드Convolutional Monge Mapping Normalization for learning on sleep data
https://github.com/qinenergy/cotta
공식 코드https://github.com/rakshith-2905/sista
공식 코드Target-Aware Generative Augmentations for Single-Shot Adaptation
https://github.com/renshengji/c-cotta
공식 코드https://github.com/renshengji/cotta
공식 코드https://github.com/robflynnyh/Self-Train-Before-You-Transcribe
공식 코드https://github.com/rochelleni/dat
공식 코드Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation
https://github.com/royzry98/moase-pytorch
공식 코드https://github.com/rt219/the-emergence-of-objectness
공식 코드The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.