Papers with Code 아카이브
Computational Efficiency
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AKASH2907/bird-species-classification
https://github.com/AKASH2907/bird_species_classification
https://github.com/AKASH2907/indian_landmark_recognition
https://github.com/AbdalaDiasse/Video-classification-for-oil-quality-estimation
https://github.com/AkhilanB/CNN-Cert
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/AmazingDay1/TAME
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/AndrewGavril/Kimera-VIO-RaspberryPi
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/AnshMittal1811/AugmentedRandomSearch
Simple random search provides a competitive approach to reinforcement learning
https://github.com/AntonioAcunzo/mvts_transformer
A Transformer-based Framework for Multivariate Time Series Representation Learning
https://github.com/Arnukk/CASPIAN
https://github.com/AsafBarZvi/Liver_project
https://github.com/Atze00/MoViNet-pytorch
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/AureliePeng/Keras-WaveletTransform
https://github.com/BboyHanat/U-Net
https://github.com/Bersaelor/AAPLMetalImageRecognition
https://github.com/Bkmz21/CompactCNNCascade
Compact Convolutional Neural Network Cascade for Face Detection
https://github.com/CESARDELATORRE/ComputerVisionTensorFlowMLNET
https://github.com/CROWN-Robustness/Crown
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models
https://github.com/Ciprian95/Licenta
https://github.com/Clemson-AI/p2gan
P$^2$-GAN: Efficient Style Transfer Using Single Style Image
https://github.com/CoCoPIE-Pruning/CoCoPIE-ModelZoo
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest
https://github.com/ContinualAI/avalanche
https://github.com/DEck13/conformal.glm
https://github.com/DEck13/paraconformal
https://github.com/DLR-RM/stable-baselines3
Simple random search provides a competitive approach to reinforcement learning
https://github.com/DT42/BerryNet
https://github.com/DanishShah/DeepDiagnosis
https://github.com/DeepMark/deepmark
https://github.com/DepthAnything/Video-Depth-Anything
Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
https://github.com/Droxef/PDMdeepsphere
https://github.com/Eacient/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/Efficient-Scalable-Machine-Learning/event-ssm
https://github.com/EighteenSprings/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.