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Test-time Adaptation

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/82magnolia/ev_tta

공식 코드

Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition

https://github.com/AliBahri94/SVWA_TTA

공식 코드

Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging

https://github.com/AronCao49/Latte

공식 코드

Reliable Spatial-Temporal Voxels For Multi-Modal Test-Time Adaptation

https://github.com/Bala93/CLIPCalib

공식 코드

Robust Calibration of Large Vision-Language Adapters

https://github.com/BaoshunWq/clip_TAEA

공식 코드

Test-time Alignment-Enhanced Adapter for Vision-Language Models

https://github.com/Chaolei98/FreeZAD

공식 코드

Training-Free Zero-Shot Temporal Action Detection with Vision-Language Models

https://github.com/Claydon-Wang/OFTTA

공식 코드

Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition

https://github.com/DebugML/sqrl

공식 코드

Do Machine Learning Models Learn Statistical Rules Inferred from Data?

https://github.com/DequanWang/tent

공식 코드

Tent: Fully Test-time Adaptation by Entropy Minimization

https://github.com/DianCh/AdaContrast

공식 코드

Contrastive Test-Time Adaptation

https://github.com/Jhyun17/DeYO

공식 코드

Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors

https://github.com/Jyxarthur/OCLR_model

공식 코드

Segmenting Moving Objects via an Object-Centric Layered Representation

https://github.com/ShambhaviCodes/CLIPOT

공식 코드

Words Matter: Leveraging Individual Text Embeddings for Code Generation in CLIP Test-Time Adaptation

https://github.com/WZq975/DA-TTA

공식 코드

Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams

https://github.com/Waffle-Liu/CEA

공식 코드

Advancing Test-Time Adaptation in Wild Acoustic Test Settings

https://github.com/abie-e/bftt3d

공식 코드

Backpropagation-free Network for 3D Test-time Adaptation

https://github.com/afsharshamsi/etage

공식 코드

ETAGE: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms for Robust Model Performance

https://github.com/aleemsidra/SaLIP

공식 코드

Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation

https://github.com/astra-vision/prolip

공식 코드

Fine-Tuning CLIP's Last Visual Projector: A Few-Shot Cornucopia

https://github.com/atschalz/dc_tabeval

공식 코드

A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data

https://github.com/baowenxuan/atp

공식 코드

Adaptive Test-Time Personalization for Federated Learning

https://github.com/benedettaliberatori/t3al

공식 코드

Test-Time Zero-Shot Temporal Action Localization

https://github.com/bit-da/abs

공식 코드

From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection

https://github.com/bit-da/dusa

공식 코드

Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation

https://github.com/bit-da/rotta

공식 코드

Robust Test-Time Adaptation in Dynamic Scenarios

https://github.com/blue-531/talos

공식 코드

TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sight

https://github.com/cadezzyr/paint

공식 코드

Dynamic Prompt Allocation and Tuning for Continual Test-Time Adaptation

https://github.com/carrtesy/m2n2

공식 코드

When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection

https://github.com/cfuchs2023/oga

공식 코드

Online Gaussian Test-Time Adaptation of Vision-Language Models

https://github.com/chen-ziyang/grata

공식 코드

Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation

https://github.com/chen-ziyang/vptta

공식 코드

Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation

https://github.com/chunmeifeng/difftpt

공식 코드

Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation

https://github.com/clement-bonnet/lpn

공식 코드

Searching Latent Program Spaces

https://github.com/compai-lab/2024-miccai-adsmi-ambekar

공식 코드

Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations

https://github.com/cuttle-fish-my/gm-guided-dg

공식 코드

Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation

https://github.com/daniellin94144/test-time-adaptation-asr-suta

공식 코드

Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition

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