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Feature Compression
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Compress data for machine interpretability to perform downstream tasks, rather than for human perception.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Jyonn/VQ4Rec
공식 코드Vector Quantization for Recommender Systems: A Review and Outlook
https://github.com/Koukyosyumei/secure_ml
공식 코드Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy
https://github.com/YannDubs/lossyless
공식 코드https://github.com/adnan-hossain/var-feat-comp
공식 코드Flexible Variable-Rate Image Feature Compression for Edge-Cloud Systems
https://github.com/adnan-hossain/var_feat_comp
공식 코드Flexible Variable-Rate Image Feature Compression for Edge-Cloud Systems
https://github.com/appletea233/llava-st
공식 코드LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understanding
https://github.com/bosszhe/emiff
공식 코드https://github.com/bosszhe/vimi
공식 코드VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection
https://github.com/ceciliasalvatore/sfcca
공식 코드Supervised Feature Compression based on Counterfactual Analysis
https://github.com/chenhongyiyang/widthformer
공식 코드WidthFormer: Toward Efficient Transformer-based BEV View Transformation
https://github.com/duanzhiihao/edge-cloud-rac
공식 코드https://github.com/gyukai/i2vc
공식 코드I$^2$VC: A Unified Framework for Intra- & Inter-frame Video Compression
https://github.com/heimine/pnc_dln
공식 코드Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination
https://github.com/jongwon20000/TRACA
공식 코드Context-aware Deep Feature Compression for High-speed Visual Tracking
https://github.com/mazumder-lab/skinnytrees
공식 코드End-to-end Feature Selection Approach for Learning Skinny Trees
https://github.com/pietro-talli/tmlcn_code
공식 코드https://github.com/rezafuru/frankensplit
공식 코드https://github.com/rezafuru/the-fool
공식 코드FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
https://github.com/shaojiawei07/BottleNetPlusPlus
공식 코드BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems
https://github.com/snailforce/sim-net
공식 코드https://github.com/viiika/Meissonic
공식 코드https://github.com/ws-syx/transvfc
공식 코드TransVFC: A Transformable Video Feature Compression Framework for Machines
https://github.com/yhbcode000/Eloss-Interpretability
공식 코드Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving
https://github.com/yingyichen-cyy/Nested-Co-teaching
공식 코드https://github.com/yoshitomo-matsubara/sc2-benchmark
공식 코드https://github.com/yoshitomo-matsubara/supervised-compression
공식 코드Supervised Compression for Resource-Constrained Edge Computing Systems
https://github.com/2023-MindSpore-1/ms-code-218/tree/main/sgcn
SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.