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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/xuxu116/pytorch-reid-lite

Spectral Feature Transformation for Person Re-identification

https://github.com/xuyxu/Deep-Clustering-Network

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

https://github.com/xyf513/SpiderCNN

SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters

https://github.com/xyntechx/Manga-Layout-Analysis

Deep Learning for Classical Japanese Literature

https://github.com/ya54/SolarUnet-magnetic-tracking

Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning

https://github.com/yandex-research/rtdl-revisiting-models

Revisiting Deep Learning Models for Tabular Data

https://github.com/yangorwell/SENG

Sketchy Empirical Natural Gradient Methods for Deep Learning

https://github.com/yangyanli/PointCNN

ABC: A Big CAD Model Dataset For Geometric Deep Learning

https://github.com/yangyucheng000/MSpaper/tree/main/u2net

Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction

https://github.com/yangyucheng000/Paper-3/tree/main/bi-crypto-nets-mindspore-master

Crypto Pump and Dump via Deep Learning Techniques

https://github.com/yangyucheng000/Papers/tree/main/STAN

Stan: Small tumor-aware network for breast ultrasound image segmentation

https://github.com/yangyucheng000/deepbsde

Solving high-dimensional partial differential equations using deep learning

https://github.com/yangyucheng000/dlrm

Deep Learning Recommendation Model for Personalization and Recommendation Systems

https://github.com/yaq007/cleverhans

The Limitations of Deep Learning in Adversarial Settings

https://github.com/yaringal/BayesianRNN

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

https://github.com/yaringal/HeteroscedasticDropoutUncertainty

Dropout as a Bayesian Approximation: Appendix

https://github.com/yaringal/multi-task-learning-example

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

https://github.com/yaziciz/GLIMS

MONAI: An open-source framework for deep learning in healthcare

https://github.com/yeliudev/catnet

Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark

https://github.com/yhcool14/nervanagpu

cuDNN: Efficient Primitives for Deep Learning

https://github.com/yibeiou/Advanced_Business_Intelligence

Wide & Deep Learning for Recommender Systems

https://github.com/yifding/deep_ed_PyTorch

Deep Joint Entity Disambiguation with Local Neural Attention

https://github.com/yih117/Analyzing-the-Generalization-Capability-of-SGLD-Using-Properties-of-Gaussian-Channels

Exploring Generalization in Deep Learning

https://github.com/yihuacheng/Dilated-Net

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

https://github.com/yihuacheng/Full-face

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

https://github.com/yihuacheng/Gaze-Net

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

https://github.com/yihuacheng/Gaze360

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

https://github.com/yihuacheng/Itracker

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

https://github.com/yihuacheng/Mnist

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

https://github.com/yihuacheng/RT-Gene

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

https://github.com/yijiazh/DFER_Summer2019

Deep Learning without Poor Local Minima

https://github.com/yikuide/graph_nets

Relational inductive biases, deep learning, and graph networks

https://github.com/yil479/yelp_review

Wide & Deep Learning for Recommender Systems

https://github.com/yinyongh/DenseRandomNet

Deep Learning in Multi-Layer Architectures of Dense Nuclei

https://github.com/yishuwei2019/DL-based-dynamic-risk-prediciton

Deep Learning for Patient-Specific Kidney Graft Survival Analysis

https://github.com/yiwang89/lululxvi-deepxde

DeepXDE: A deep learning library for solving differential equations

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