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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/drscotthawley/SPNet

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/dsba-lab/openal

Deep Bayesian Active Learning with Image Data

https://github.com/dscarmo/E2DConsHipseg

Hippocampus Segmentation on Epilepsy and Alzheimer's Disease Studies with Multiple Convolutional Neural Networks

https://github.com/duanders/mpi_learn

Deep learning with Elastic Averaging SGD

https://github.com/duanyzhi/graph_net

Relational inductive biases, deep learning, and graph networks

https://github.com/dxw1997/U-project

Enforcing temporal consistency in Deep Learning segmentation of brain MR images

https://github.com/eabarnes1010/controlled_abstention_networks

Combating Label Noise in Deep Learning Using Abstention

https://github.com/edi-riga/handwash

Automated Quality Assessment of Hand Washing Using Deep Learning

https://github.com/ee17b031-iittp/MNIST-Retrain-with-DeepXplore

DeepXplore: Automated Whitebox Testing of Deep Learning Systems

https://github.com/ee17b031-iittp/Projected-Gradient-Descent-with-CIFAR10

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/eho-tacc/DeepLabCut

Deep learning tools for the measurement of animal behavior in neuroscience

https://github.com/ekolodyazhnay/test-ds

A Deep-Learning-Based Fashion Attributes Detection Model

https://github.com/ektas0330/cell-segmentation

Deep Learning-Based Semantic Segmentation of Microscale Objects

https://github.com/eldadp100/Towards-Deep-Learning-Models-Resistant-to-Adversarial-Attacks-Implementation

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/eldadp100/cnn_course_final

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/elia-mercatanti/deep-learning-symbolic-mathematics

Deep Learning for Symbolic Mathematics

https://github.com/elias-sundqvist/obsidian-annotator

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

https://github.com/elieJalbout/Clustering-with-Deep-learning

Clustering with Deep Learning: Taxonomy and New Methods

https://github.com/elisiojsj/Kuzushiji-49

Deep Learning for Classical Japanese Literature

https://github.com/elites2k19/prism-attack

The Limitations of Deep Learning in Adversarial Settings

https://github.com/elliewith/XOmiVAE

XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

https://github.com/emiresenov/jaxpi

PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

https://github.com/enouenj/sparse-interaction-additive-networks

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

https://github.com/eole-nlp/eole

Revisiting BFloat16 Training

https://github.com/erictiandong/drn

Distributional Refinement Network: Distributional Forecasting via Deep Learning

https://github.com/ericyang789/Parallel-Compute-Project

Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis

https://github.com/ernestkck/RML2018

Over the Air Deep Learning Based Radio Signal Classification

https://github.com/evangravelle/AI-projects

Thinking Fast and Slow with Deep Learning and Tree Search

https://github.com/evtimovi/robust_physical_perturbations

Robust Physical-World Attacks on Deep Learning Models

https://github.com/f-dangel/curvlinops

Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra

https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset

When Deep Learning Met Code Search

https://github.com/facebookresearch/SymbolicMathematics

Deep Learning for Symbolic Mathematics

https://github.com/facebookresearch/deep-variance-reduction

On the Ineffectiveness of Variance Reduced Optimization for Deep Learning

https://github.com/facebookresearch/disentangling-correlated-factors

Deep Learning Face Attributes in the Wild

https://github.com/facebookresearch/fairscale

AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks

https://github.com/facebookresearch/pytorch-dp

Deep Learning with Differential Privacy

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