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Deep Learning

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

https://github.com/ThomasRobertFr/deep-learning-figures

SHADE: Information-Based Regularization for Deep Learning

https://github.com/TiagoCortinhal/SalsaNext

Deep Learning for 3D Point Clouds: A Survey

https://github.com/TinfoilHat0/Learning-to-Reweight-Examples-for-Robust-Deep-Learning-with-PyTorch-Higher

Learning to Reweight Examples for Robust Deep Learning

https://github.com/Tom-900/3D-Tensor-based-Deep-Learning-Models-for-Predicting-Option-Price

3D Tensor-based Deep Learning Models for Predicting Option Price

https://github.com/Tommy-Ngx/AM-MobileNet1D

AM-MobileNet1D: A Portable Model for Speaker Recognition

https://github.com/Tommy-Ngx/MobileNetV1_SR

AM-MobileNet1D: A Portable Model for Speaker Recognition

https://github.com/TonyYaoMSU/ProjectedGradientDescent

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/TranSys2020/TranSys

Interpreting Deep Learning-Based Networking Systems

https://github.com/Trevillie/MagNet

MagNet: a Two-Pronged Defense against Adversarial Examples

https://github.com/TruongThaoNguyen/kakurenbo

Accelerating Deep Learning by Focusing on the Biggest Losers

https://github.com/UlionTse/mlgb

Wide & Deep Learning for Recommender Systems

https://github.com/UlysseCoteAllard/MyoArmbandDataset

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

https://github.com/VadymV/ICOS

Are we done with object recognition? The iCub robot's perspective

https://github.com/Veloc1tyE/Drift

Applications of Deep Learning to Nuclear Fusion Research

https://github.com/VhalPurohit/290s

Stochastic Gradient Push for Distributed Deep Learning

https://github.com/VishaalMK/VectorDefense

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/VisionSystemsInc/nervanagpu

cuDNN: Efficient Primitives for Deep Learning

https://github.com/Waino/hnmt

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

https://github.com/WayneDW/Bayesian-Sparse-Deep-Learning

An Adaptive Empirical Bayesian Method for Sparse Deep Learning

https://github.com/WayneDW/Contour-Stochastic-Gradient-Langevin-Dynamics

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

https://github.com/Westlake-AI/Markov-Lipschitz-Deep-Learning

Markov-Lipschitz Deep Learning

https://github.com/Westlake-AI/openmixup

Self-Tuning for Data-Efficient Deep Learning

https://github.com/WhiTExB3AR/Emotion_Diary

Facial Motion Prior Networks for Facial Expression Recognition

https://github.com/WhiTExB3AR/PreProduceCode-FMPN-FER

Facial Motion Prior Networks for Facial Expression Recognition

https://github.com/Wslsdx/Deep-Learning-Approach-for-Surface-Defect-Detection

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

https://github.com/XavierJiezou/cloud-removal-deploy

A Remote Sensing Image Dataset for Cloud Removal

https://github.com/XavierSpycy/tabtransformers

Revisiting Deep Learning Models for Tabular Data

https://github.com/XiaoxiangLin/DCN-keras

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

https://github.com/XinshaoAmosWang/DerivativeManipulation

Normalized Loss Functions for Deep Learning with Noisy Labels

https://github.com/XinshaoAmosWang/Emphasis-Regularisation-by-Gradient-Rescaling

Normalized Loss Functions for Deep Learning with Noisy Labels

https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE

Normalized Loss Functions for Deep Learning with Noisy Labels

https://github.com/XiwenChen-NAU/DeepDIH

Deep DIH : Statistically Inferred Reconstruction of Digital In-Line Holography by Deep Learning

https://github.com/Xtra-Computing/VertiBench

Split Learning for collaborative deep learning in healthcare

https://github.com/YafeiWu/DIEN

Deep Interest Evolution Network for Click-Through Rate Prediction

https://github.com/YasodGinige/TrafficGPT

Automated Website Fingerprinting through Deep Learning

https://github.com/YeongHyeon/SWA-Gaussian-TF2

A Simple Baseline for Bayesian Uncertainty in Deep Learning

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