https://github.com/Elman295/MMD-GAN
MMD GAN: Towards Deeper Understanding of Moment Matching Network
Papers with Code 아카이브
Computational Efficiency
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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/Elstuhn/Video-object-detection-by-location-anticipation
Objects do not disappear: Video object detection by single-frame object location anticipation
https://github.com/Emyyr/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/Eom-taeseon/CV_SceneTextRecognition
Vision Transformer for Fast and Efficient Scene Text Recognition
https://github.com/Ericzhang1/BAGAU-Net
https://github.com/Evezerest/DocRec
https://github.com/Fancyssc/Spiking-Transformers
TIM: An Efficient Temporal Interaction Module for Spiking Transformer
https://github.com/FeiLiu36/LLM4MOEA
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks
https://github.com/FourthM/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/GXU-GMU-MICCAI/AdaBatch-numerical-experiments
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
https://github.com/GeoVision-Lab/Geoseg
Geoseg: A Computer Vision Package for Automatic Building Segmentation and Outline Extraction
https://github.com/Gothos/LRU-pytorch
https://github.com/GraphPKU/Refined-GAE
https://github.com/GuanZihan/verification
https://github.com/HardikJ7321/AugmentedRandomSearch-AI-
Simple random search provides a competitive approach to reinforcement learning
https://github.com/IAHispano/Applio/tree/exp/vocoders/rvc/lib/algorithm
https://github.com/IBM/CROWN-Robustness-Certification
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/IBM/SIC
https://github.com/IMvision12/keras-vision-models
https://github.com/IRVLab/SUIM
Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
https://github.com/IRVLab/SUIM-Net
Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
https://github.com/JanMarcelKezmann/Residual-Network-Architectures
https://github.com/JasonZK/Soccer_OCR
https://github.com/Jayanthipalanichamy/ElucidateTheScene
https://github.com/JeffJeffy/CS224W-OGB-DEA-JK
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
https://github.com/Ji4chenLi/ARS_experiment
Simple random search provides a competitive approach to reinforcement learning
https://github.com/JiangWeibeta/Checkerboard-Context-Model-for-Efficient-Learned-Image-Compression
Checkerboard Context Model for Efficient Learned Image Compression
https://github.com/Jiaxin-Ye/DepMamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/JohnRomanelis/SPVD_Lightning
Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models
https://github.com/JonasFrey96/Kimera-Semantics
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/KegangWangCCNU/RLAP-dataset
Camera-Based HRV Prediction for Remote Learning Environments
https://github.com/KhurramPirov/bayesian_Inverse
https://github.com/KyanChen/RSMamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/KyleYoung1997/DNNorDermatologist
https://github.com/LKLQQ/Inception-v2
https://github.com/LUCKMOONLIGHT/SLRDet
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.