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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/andreacos/gan-generated-face-detection

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/andrerclaudio/channel_estimation

Deep Learning-Based Channel Estimation

https://github.com/andrewilyas/ens-adv-train-attack

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/aneeqr/Object-Detection

CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization

https://github.com/angie-lab-jhu/density_softmax

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning

https://github.com/anita76/linecapdataset

LineCap: Line Charts for Data Visualization Captioning Models

https://github.com/ankit-vaghela30/Cilia-Segmentation

Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION

Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning

https://github.com/annieplouis/craic

Deep Learning to Detect Redundant Method Comments

https://github.com/anon767/CayleyNet

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

https://github.com/anupamme/SymbolicMathematics

Deep Learning for Symbolic Mathematics

https://github.com/anuragreddygv323/Important-stuff

Deep Learning based Recommender System: A Survey and New Perspectives

https://github.com/aolmin/active_learning_weak_sup

Deep Bayesian Active Learning with Image Data

https://github.com/apacha/OMR-Datasets

Optical Music Recognition with Convolutional Sequence-to-Sequence Models

https://github.com/apple/ml-flair

Deep Learning with Differential Privacy

https://github.com/applecrazy/FruitClassifier

Fruit recognition from images using deep learning

https://github.com/aprincemohamed/data-driven-adversarial-amc

Data-Driven Subsampling in the Presence of an Adversarial Actor

https://github.com/aravic/graph_nets

Relational inductive biases, deep learning, and graph networks

https://github.com/arnauldnzegha/deep2pde_Berg_Nystrom

Data-driven discovery of PDEs in complex datasets

https://github.com/arobey1/advbench

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/artitw/tensor2tensor

Tensor2Tensor for Neural Machine Translation

https://github.com/aruberts/TabTransformerTF

Revisiting Deep Learning Models for Tabular Data

https://github.com/ashantanu/DCGAN

A guide to convolution arithmetic for deep learning

https://github.com/asharakeh/probdet

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

https://github.com/asleepzzz/freeze_miopen

MIOpen: An Open Source Library For Deep Learning Primitives

https://github.com/asleepzzz/nhwc_miopen

MIOpen: An Open Source Library For Deep Learning Primitives

https://github.com/asleepzzz/nhwc_shuffle

MIOpen: An Open Source Library For Deep Learning Primitives

https://github.com/astorfi/deep-clustering-kmeans

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

https://github.com/astoycos/Mini_Project2

Deep Learning: A Critical Appraisal

https://github.com/atapritchard/DPDEs

DGM: A deep learning algorithm for solving partial differential equations

https://github.com/atasoyhus/CeNiN

cuDNN: Efficient Primitives for Deep Learning

https://github.com/athar71/Object_Detection_Enhancement_with_RBG-D_Data

Multimodal Deep Learning for Robust RGB-D Object Recognition

https://github.com/atilberk/evidential-deep-learning-to-quantify-classification-uncertainty

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/atriumlts/subpixel

A guide to convolution arithmetic for deep learning

https://github.com/automl/neps

PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning

https://github.com/avillemin/GANs

A guide to convolution arithmetic for deep learning

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