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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/nayash/shortest-distance-approx-deep-learning

Shortest path distance approximation using deep learning techniques

https://github.com/ndb796/pytorch-adversarial-training-cifar

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/nebyu08/tiny_recommendation

Wide & Deep Learning for Recommender Systems

https://github.com/neshitov/UNet

Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

https://github.com/neural-structured-additive-learning/deepregression

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

https://github.com/neuro-ml/resnet_cnn_mri_adni

Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification

https://github.com/newspapersegmentation/newspaperimagesegmentation

dhSegment: A generic deep-learning approach for document segmentation

https://github.com/nicola-decao/s-vae

TensorFlow Distributions

https://github.com/nicola-decao/s-vae-tf

TensorFlow Distributions

https://github.com/nikithamalgifb/dlrm

Deep Learning Recommendation Model for Personalization and Recommendation Systems

https://github.com/nikulshr/resnet_fpga

Over the Air Deep Learning Based Radio Signal Classification

https://github.com/nmhkahn/CARN-pytorch

Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network

https://github.com/nnaisense/pytorch_sym3eig

Deep Iterative Surface Normal Estimation

https://github.com/noise-learning/selfmix

Label Noise Types and Their Effects on Deep Learning

https://github.com/npurg/faknow

Fake News Detection on Social Media using Geometric Deep Learning

https://github.com/nrel/m2p

Relational inductive biases, deep learning, and graph networks

https://github.com/ntia/alignnet

MOSNet: Deep Learning based Objective Assessment for Voice Conversion

https://github.com/nvidia/transformerengine

FP8 Formats for Deep Learning

https://github.com/nvlabs/coat

FP8 Formats for Deep Learning

https://github.com/nvlabs/gnn-decoder

Learning to Decode Linear Codes Using Deep Learning

https://github.com/nyak10/mwc

Using Deep Learning for Image-Based Plant Disease Detection

https://github.com/oatml-markslab/proteinnpt

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning

https://github.com/ogunlao/saint

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

https://github.com/okrasolar/pytorch-timeseries

InceptionTime: Finding AlexNet for Time Series Classification

https://github.com/oleg-kufirin/recognizing-handwritten-hiragana-symbols

Deep Learning for Classical Japanese Literature

https://github.com/omidmn/Engagement-Recognition

Automatic Recognition of Student Engagement using Deep Learning and Facial Expression

https://github.com/omidnezami/Engagement-Recognition

Automatic Recognition of Student Engagement using Deep Learning and Facial Expression

https://github.com/online-ml/river

Green AI

https://github.com/opaliss/dmd_autoencoder

Deep learning for universal linear embeddings of nonlinear dynamics

https://github.com/openai/cleverhans

The Limitations of Deep Learning in Adversarial Settings

https://github.com/openclimatefix/graph_weather

A Foundation Model for the Earth System

https://github.com/opera-adt/distmetrics

Deep Learning-based Damage Mapping with InSAR Coherence Time Series

https://github.com/orendv/learning_to_sample

MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling

https://github.com/oshapio/Localized-CNNs-for-Geospatial-Wind-Forecasting

Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting

https://github.com/osmr/imgclsmob

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/ostadabbas/in-bed-pose-estimation

In-Bed Pose Estimation: Deep Learning with Shallow Dataset

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