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/LeapMind/PUAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/LeeJunHyun/Image_Segmentation
https://github.com/Leeroo-AI/mergoo
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/LiChenyang-Github/Temporal-Transformer-Module
https://github.com/LinZhu111/EVSNN
Event-based Video Reconstruction via Potential-assisted Spiking Neural Network
https://github.com/Liuyubao/transfer-learning
https://github.com/LuCeHe/lru_unofficial
https://github.com/MIT-SPARK/Kimera-Evaluation
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MIT-SPARK/Kimera-RPGO
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MIT-SPARK/Kimera-Semantics
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MIT-SPARK/Kimera-VIO
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MIT-SPARK/Kimera-VIO-Evaluation
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MIT-SPARK/Kimera-VIO-ROS
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/inception_v3
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/vgg
https://github.com/MS-P3/code6/tree/main/rag
RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
https://github.com/MagaliDrumare/Augmented-Random-Search-ARS-Intuition
Simple random search provides a competitive approach to reinforcement learning
https://github.com/MasoumehVahedi/Brain_MRI_Segmentation
https://github.com/MatusPilnan/nsiete-project
https://github.com/Mayurji/Image-Classification-PyTorch
https://github.com/Meghraj-Webllisto/stylegan
https://github.com/MeiguangJin/Learning-to-Extract-a-Video-Sequence-from-a-Single-Motion-Blurred-Image
Learning to Extract a Video Sequence from a Single Motion-Blurred Image
https://github.com/Mengyanz/Mengyanz.github.io
Two-Stage Neural Contextual Bandits for Personalised News Recommendation
https://github.com/Mind23-2/MindCode-3/tree/main/MIMO-UNet
Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression
https://github.com/MindCode-4/code-12/tree/main/lru
https://github.com/MindSpore-paper-code-3/code2/tree/main/cspdarknet53
https://github.com/MindSpore-scientific-2/code-10/tree/main/TSA_mindspore
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
https://github.com/MindSpore-scientific/code-13/tree/main/TSA_mindspore
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
https://github.com/MindSpore-scientific/code-5/tree/main/sa-mlp-distilling-graph
https://github.com/MindSpore-scientific/code-8/tree/main/Simba
SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
https://github.com/Minhchuyentoancbn/Continual-Learning
https://github.com/MoaazK/comp511-project
https://github.com/MoaazK/unet-transformer
https://github.com/MohammedEltoum/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/Mohanned-Elkholy/BigGan-model-inversion-with-image-transformations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.