https://github.com/mlgig/amee
공식 코드Robust Explainer Recommendation for Time Series Classification
Papers with Code 아카이브
Human Activity Recognition
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Classify various human activities
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mmalekzadeh/dana
공식 코드DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data
https://github.com/navidfoumani/tsc_survey
공식 코드Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
https://github.com/ngk03/dl_ppg_review
공식 코드A Review of Deep Learning Methods for Photoplethysmography Data
https://github.com/nipdep/SEZ-HARN
공식 코드SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition Network
https://github.com/nkatzz/OLED
공식 코드Semi-Supervised Online Structure Learning for Composite Event Recognition
https://github.com/parhamhadikhani/human-activity-discovery-hpgmk
공식 코드https://github.com/percom-submission/PerCom-2023
공식 코드Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition
https://github.com/philipph77/ts-moco
공식 코드TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
https://github.com/prijatelj/human-activity-recognition-in-an-open-world
공식 코드https://github.com/razzu/cmc-cmkm
공식 코드Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition
https://github.com/realcrane/BASAR-Black-box-Attack-on-Skeletal-Action-Recognition
공식 코드https://github.com/realcrane/Defending-Black-box-Skeleton-based-Human-Activity-Classifiers
공식 코드Defending Black-box Skeleton-based Human Activity Classifiers
https://github.com/realcrane/robustactionrecogniser
공식 코드Defending Black-box Skeleton-based Human Activity Classifiers
https://github.com/rmazzier/star
공식 코드Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction
https://github.com/rogetk/oddet
공식 코드https://github.com/ruizehan/par
공식 코드https://github.com/saif-mahmud/hierarchical-attention-HAR
공식 코드Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition
https://github.com/saif-mahmud/self-attention-HAR
공식 코드Human Activity Recognition from Wearable Sensor Data Using Self-Attention
https://github.com/sandeep-189/data-augmentation
공식 코드Transformer Networks for Data Augmentation of Human Physical Activity Recognition
https://github.com/scut-cszcl/sfusion
공식 코드SFusion: Self-attention based N-to-One Multimodal Fusion Block
https://github.com/sdimi/Step2heart
공식 코드Learning Generalizable Physiological Representations from Large-scale Wearable Data
https://github.com/sez-harn/sez-harn
공식 코드SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition Network
https://github.com/signal-park/resolution-adaptive-spectrogram
공식 코드Resolution-Adaptive Micro-Doppler Spectrogram for Human Activity Recognition
https://github.com/signetlabdei/sharp
공식 코드https://github.com/sperimental3/charon
공식 코드Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
https://github.com/srvCodes/continual-learning-benchmark
공식 코드Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis
https://github.com/sshao2013/convboost
공식 코드ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition
https://github.com/stilllearningsoumya/acconotate
공식 코드Accoustate: Auto-annotation of IMU-generated Activity Signatures under Smart Infrastructure
https://github.com/strohmayerj/datta
공식 코드https://github.com/strohmayerj/wiflexformer
공식 코드https://github.com/t-hasegawa-fu/easy-ensemble
공식 코드Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition
https://github.com/the-sky001/widistill
공식 코드WiDistill: Distilling Large-scale Wi-Fi Datasets with Trajectory Matching
https://github.com/themaxiao/ditmos
공식 코드DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
https://github.com/thuhci/MMTSA
공식 코드MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity Recognition
https://github.com/toytiny/milliflow
공식 코드milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.