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/catalpaaa/demansia
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/chengding0713/awesome-ppg-af-detection
https://github.com/chenhongruixuan/mambacd
https://github.com/chenller/mmseg-extension
BiFormer: Vision Transformer with Bi-Level Routing Attention
https://github.com/chintan1995/Image-Denoising-using-Deep-Learning
https://github.com/chrhenning/hypercl
https://github.com/chumingqian/Model_Compression_For_YOLOV3-V4
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
https://github.com/cinout/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/cocofeat/tbrats
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty
https://github.com/code-implementation1/Code5/tree/main/MIMO-UNet
Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression
https://github.com/code-implementation1/Code9/tree/main/PVNet
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
https://github.com/code-implementation1/Code9/tree/main/PointNet
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
https://github.com/codelion/adaptive-classifier
https://github.com/collabora/whisperspeech
https://github.com/cor3bit/bertsekas-marl
https://github.com/corels/corels
https://github.com/corels/rcppcorels
https://github.com/cpow-89/Augmented-Random-Search
Simple random search provides a competitive approach to reinforcement learning
https://github.com/craftGBD/caffe-GBD
https://github.com/cran/BDgraph
Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models
https://github.com/crimama/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/csinva/imodels
https://github.com/danielbis/python_cheap_fashion
https://github.com/danielenricocahall/Keras-UNet
https://github.com/danielsoy/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/danielsoy/EfficientAD-1
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/daniilrobnikov/vits2
https://github.com/data-science-in-mechanical-engineering/hci-gibo
Simple random search provides a competitive approach to reinforcement learning
https://github.com/davidmrau/mixture-of-experts
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
https://github.com/dc-dc-dc/mlx-rwkv
https://github.com/ddcas/p2gan-pytorch
P$^2$-GAN: Efficient Style Transfer Using Single Style Image
https://github.com/dellysursh/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/dengyu-wu/aoi-snn-regularisation
Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
https://github.com/djstrouse/information-bottleneck
https://github.com/dmitrii-marin/adaptive-sampling
Efficient Segmentation: Learning Downsampling Near Semantic Boundaries
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.