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/Mushroomcat9998/PaddleOCR

PP-OCR: A Practical Ultra Lightweight OCR System

https://github.com/NIRVANALAN/ClusterGCN_google-reseach

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

https://github.com/NVIDIA/modulus-makani

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale

https://github.com/NeelBhowmik/efficient-compact-fire-detection-cnn

Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

https://github.com/NeuroDataDesign/mgcpy

Discovering and Deciphering Relationships Across Disparate Data Modalities

https://github.com/Nikhil9786/Implemetation-of-VGG16-ResNet18-InceptionV2-on-Cifar100

Rethinking the Inception Architecture for Computer Vision

https://github.com/Nikolai10/Checkerboard-Context-Model

Checkerboard Context Model for Efficient Learned Image Compression

https://github.com/OneYearIsEnough/PaddleOCR-Recog-LmdbDataset-Conversion

PP-OCR: A Practical Ultra Lightweight OCR System

https://github.com/OpenLLMAI/OpenLLaMA2

REINFORCE++: A Simple and Efficient Approach for Aligning Large Language Models

https://github.com/OpenLLMAI/OpenRLHF

REINFORCE++: A Simple and Efficient Approach for Aligning Large Language Models

https://github.com/PaddlePaddle/PLSC

ConvMAE: Masked Convolution Meets Masked Autoencoders

https://github.com/PaddlePaddle/PaddleClas

Rethinking the Inception Architecture for Computer Vision

https://github.com/PaddlePaddle/PaddleSeg

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/PerfectExamples/Perfect-TensorFlow-Demo-Vision

Rethinking the Inception Architecture for Computer Vision

https://github.com/Peyman-HK/De-randomized-HiDe-MK

Deep neural networks with controlled variable selection for the identification of putative causal genetic variants

https://github.com/PlumedSerpent/tmp_perspective_map

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/PointsCoder/VOTR

Voxel Transformer for 3D Object Detection

https://github.com/PratikTayshete/Cats-and-Dogs-Image-Classification

Rethinking the Inception Architecture for Computer Vision

https://github.com/Puzer/stylegan-encoder

Rethinking the Inception Architecture for Computer Vision

https://github.com/QData/JointNets

A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models

https://github.com/Radvian/apple-img

Rethinking the Inception Architecture for Computer Vision

https://github.com/Radvian/apple_img

Rethinking the Inception Architecture for Computer Vision

https://github.com/RemiArbache/style-transfer-M2

Rethinking the Inception Architecture for Computer Vision

https://github.com/RingBDStack/DRL-DBSCAN/tree/main/DRL-DBSCAN-mindspore

Automating DBSCAN via Deep Reinforcement Learning

https://github.com/Robinatp/Tensorflow_Model_Inception

Rethinking the Inception Architecture for Computer Vision

https://github.com/RudreshVeerkhare/StyleGan

Rethinking the Inception Architecture for Computer Vision

https://github.com/SINGROUP/ED-AFM

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/STAC-USC/graph_learning_properties

Graph Learning from Data under Structural and Laplacian Constraints

https://github.com/Sakib1263/Inception-InceptionResNet-1D-2D-Tensorflow-Keras

Rethinking the Inception Architecture for Computer Vision

https://github.com/Sakib1263/Inception-Model-Builder-Tensorflow-Keras

Rethinking the Inception Architecture for Computer Vision

https://github.com/SamArgt/AudioSourceSep

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

https://github.com/Schlam/inceptionv3-transfer-learning

Rethinking the Inception Architecture for Computer Vision

https://github.com/ShadenSmith/ghpages-test

ZeRO-Offload: Democratizing Billion-Scale Model Training

https://github.com/Shakib-IO/Diminishing_Image_Noise_Using_Deep_Learning

Multi-level Wavelet-CNN for Image Restoration

https://github.com/Shrajan/AAAI-2022

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/Shrajan/IB_U_Nets

Attention U-Net: Learning Where to Look for the Pancreas

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.