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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/braceal/DeepDriveMD

DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding

https://github.com/brendon-boldt/xferbench

Emergent Multi-Agent Communication in the Deep Learning Era

https://github.com/brmson/dataset-sts

Deep Learning for Answer Sentence Selection

https://github.com/brunocalogero/AMLSassignment

From Facial Parts Responses to Face Detection: A Deep Learning Approach

https://github.com/bryanyzhu/two-stream-pytorch

A Comprehensive Study of Deep Video Action Recognition

https://github.com/bt-s/Split-Learning-and-Federated-Learning

Split learning for health: Distributed deep learning without sharing raw patient data

https://github.com/bumuckl/SemiSupervisedDLForFCNs

Semi-Supervised Deep Learning for Fully Convolutional Networks

https://github.com/bxz9200/ultraclean

Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

https://github.com/byschii/ecg-segmentation/blob/main/unet_for_ecg.ipynb

Deep Learning for ECG Segmentation

https://github.com/bytedance/largebatchctr

Wide & Deep Learning for Recommender Systems

https://github.com/c3210927/point_cnn

ABC: A Big CAD Model Dataset For Geometric Deep Learning

https://github.com/caijie0620/openrr-1k

Survey on Single-Image Reflection Removal using Deep Learning Techniques

https://github.com/cambridge-mlg/miracle

Bayesian Compression for Deep Learning

https://github.com/cambridge-mlg/variational-shannon-coding

Bayesian Compression for Deep Learning

https://github.com/capptcha/capptcha.github.io

Deceiving computers in Reverse Turing Test through Deep Learning

https://github.com/carlr67/deephitplus

Feature Selection for Survival Analysis with Competing Risks using Deep Learning

https://github.com/carnotaur/crnn-tutorial

Scene Text Detection and Recognition: The Deep Learning Era

https://github.com/carsonyang518/aaai24-aes-afg

SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring

https://github.com/catta202000/CMRNet

CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps

https://github.com/cattaneod/CMRNet

CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps

https://github.com/cbsudux/Mumbai-slum-segmentation

Slum Segmentation and Change Detection : A Deep Learning Approach

https://github.com/cci-bonn/hd-glio

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

https://github.com/ccsc-tools/SolarUnet-magnetic-tracking

Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning

https://github.com/cdluminate/advrank

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/cdluminate/advrank-pub

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/ced-kin/dog-breed-ai

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/cerndb/dist-keras

Deep learning with Elastic Averaging SGD

https://github.com/cersar/3D_detection

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/ceteri/pytextrank

Energy and Policy Considerations for Deep Learning in NLP

https://github.com/cfernandezlab/Lines-and-Vanishing-Points-directly-on-Panoramas

Layouts from Panoramic Images with Geometry and Deep Learning

https://github.com/chaeger/upper_capacity_bounds

Deep Learning for Channel Coding via Neural Mutual Information Estimation

https://github.com/chainer/chainercv

ChainerCV: a Library for Deep Learning in Computer Vision

https://github.com/chandar-lab/cmoptimizer

Memory Augmented Optimizers for Deep Learning

https://github.com/charan223/Brain-Tumor-Segmentation-using-Topological-Loss

NiftyNet: a deep-learning platform for medical imaging

https://github.com/charan223/brain_tumor_topology

NiftyNet: a deep-learning platform for medical imaging

https://github.com/charan223/topology-aware-networks

NiftyNet: a deep-learning platform for medical imaging

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