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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/MayMiao0923/PESQ

MOSNet: Deep Learning based Objective Assessment for Voice Conversion

https://github.com/MaziarMF/deep-k-means

Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering

https://github.com/MehranSoltani94/ChannelNet

Deep Learning-Based Channel Estimation

https://github.com/MikelBros/Deep_Spammer_Detection_GCN

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

https://github.com/MilesCranmer/lagrangian_nns

Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

https://github.com/Mind23-2/MindCode-122

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/Mind23-2/MindCode-159

Seq2Seq AI Chatbot with Attention Mechanism

https://github.com/MindCode-4/code-11/tree/main/Adaptive-Nesterov-Momentum-Algorithm

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

https://github.com/MindCode-4/code-13/tree/main/RAF-ms-main

RAF: Holistic Compilation for Deep Learning Model Training

https://github.com/MindCode-4/code-13/tree/main/smu

SMU: smooth activation function for deep networks using smoothing maximum technique

https://github.com/MindCode-4/code-13/tree/main/stable-sam

Stable Architectures for Deep Neural Networks

https://github.com/MindCode-4/code-14/tree/main/AGW

Deep Learning for Person Re-identification: A Survey and Outlook

https://github.com/MindCode-4/code-4/tree/main/rag

RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms

https://github.com/MindCode-4/code-5/tree/main/van

VanillaNet: the Power of Minimalism in Deep Learning

https://github.com/MindCode-4/code-6/tree/main/Adaptive-Nesterov-Momentum-Algorithm

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

https://github.com/MindCode-4/code-7/tree/main/Learning-with-Noisy-Labels

Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels

https://github.com/MindCode-4/code-8/tree/main/RAF-ms-main

RAF: Holistic Compilation for Deep Learning Model Training

https://github.com/MindCode-4/code-8/tree/main/multi-task-learning-using-uncertainty-to

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/MindSpore-MS-Code2/code0/tree/main/dpn

DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy

https://github.com/MindSpore-paper-code-2/code3/tree/main/vanillanet

VanillaNet: the Power of Minimalism in Deep Learning

https://github.com/MindSpore-paper-code-2/code400/tree/main/Inception/xception

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/MindSpore-paper-code-3/code10/tree/main/fishnet99

FishNet: A Camera Localizer using Deep Recurrent Networks

https://github.com/MindSpore-paper-code-3/code4/tree/main/hed

HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

https://github.com/MindSpore-paper-code-3/code6/tree/main/AdderNGD

AdderNet: Do We Really Need Multiplications in Deep Learning?

https://github.com/MindSpore-paper-code-3/code9/tree/main/textrcnn

Deep Learning Based Text Classification: A Comprehensive Review

https://github.com/MindSpore-scientific-2/code-1/tree/main/ZoneoutRNN

RNNoise-Ex: Hybrid Speech Enhancement System based on RNN and Spectral Features

https://github.com/MindSpore-scientific-2/code-3/tree/main/wide-minima-density-hypothesis

Wide & Deep Learning for Recommender Systems

https://github.com/MindSpore-scientific/code-10/tree/main/ZoneoutRNN

RNNoise-Ex: Hybrid Speech Enhancement System based on RNN and Spectral Features

https://github.com/MindSpore-scientific/code-13/tree/main/Last-Query-Transformer-RNN

RNNoise-Ex: Hybrid Speech Enhancement System based on RNN and Spectral Features

https://github.com/MindSpore-scientific/code-3/tree/main/Adaptive-Nesterov-Momentum-Algorithm

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

https://github.com/MindSpore-scientific/code-6/tree/main/multi-task-learning-using-uncertainty-to

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/Mingyuan-Zhu/BCI_practice

Deep learning-based electroencephalography analysis: a systematic review

https://github.com/Mohit-Mithra/ceiling-graph

Temporal Graph Networks for Deep Learning on Dynamic Graphs

https://github.com/Monireh2/kg-deductive-reasoner

Reasoning over RDF Knowledge Bases using Deep Learning

https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/NIDQ/Deep-Learning-Approach-for-Surface-Defect-Detection-For-Fun

Segmentation-Based Deep-Learning Approach for Surface-Defect Detection

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