https://github.com/eunjin93/SICR_BCI
공식 코드Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ewellchen/da-stin
공식 코드Deep Domain Adversarial Adaptation for Photon-efficient Imaging
https://github.com/ewellchen/stin
공식 코드Deep Domain Adversarial Adaptation for Photon-efficient Imaging
https://github.com/ewsn1593/hdrq
공식 코드Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation
https://github.com/exathlonbenchmark/divad
공식 코드Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains
https://github.com/excitedkid/vipaug
공식 코드https://github.com/exitudio/GaitSADA
공식 코드GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition
https://github.com/explainableml/acvc
공식 코드Attention Consistency on Visual Corruptions for Single-Source Domain Generalization
https://github.com/eyalbd2/PADA
공식 코드PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
https://github.com/eyalbd2/PERL
공식 코드PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models
https://github.com/eyalbd2/scratchda
공식 코드https://github.com/facebookresearch/ConvNeXt
공식 코드https://github.com/facebookresearch/DomainBed
공식 코드https://github.com/facebookresearch/InvarianceUnitTests
공식 코드Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization Algorithms
https://github.com/facebookresearch/InvariantRiskMinimization
공식 코드https://github.com/facebookresearch/ModelRatatouille
공식 코드Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization
https://github.com/facebookresearch/ResNeXt
공식 코드Aggregated Residual Transformations for Deep Neural Networks
https://github.com/facebookresearch/Whac-A-Mole
공식 코드A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
https://github.com/facebookresearch/XRM
공식 코드https://github.com/facebookresearch/adaptive_teacher
공식 코드https://github.com/facebookresearch/dinov2
공식 코드https://github.com/facebookresearch/fairseq
공식 코드https://github.com/facebookresearch/icp-block-mdp
공식 코드Learning Domain Invariant Representations in Goal-conditioned Block MDPs
https://github.com/facebookresearch/latent-treelstm
공식 코드https://github.com/facebookresearch/mae
공식 코드https://github.com/facebookresearch/mixup-cifar10
공식 코드https://github.com/facebookresearch/vissl
공식 코드Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
https://github.com/fandulu/IHDA
공식 코드Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval
https://github.com/fang-zhen/Open-set-domain-adaptation
공식 코드https://github.com/faresbougourzi/awesome-dl-for-medical-imaging-segmentation
공식 코드https://github.com/farinamatteo/zero
공식 코드Frustratingly Easy Test-Time Adaptation of Vision-Language Models
https://github.com/fayeben/adas
공식 코드UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation Pipeline
https://github.com/fchiaroni/clustering_softmax_predictions
공식 코드Simplex Clustering via sBeta with Applications to Online Adjustment of Black-Box Predictions
https://github.com/fdalvi/NeuroX
공식 코드https://github.com/fe1ixxu/Gradual-Finetune
공식 코드https://github.com/feipan664/IntraDA
공식 코드Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.