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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/stop68/remote-sensing-image-classification

Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image Classification

https://github.com/stupidbuluchacha/urbanclip

Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook

https://github.com/subex/STDW

Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method

https://github.com/subhobrata/Courses_ML_DL4

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

https://github.com/sudipansaha/ultrasoundACGANClassification

Ultrasound Image Classification using ACGAN with Small Training Dataset

https://github.com/sudohainguyen/STAN-small-tumor-aware-network

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

https://github.com/sunblaze-ucb/dpml-benchmark

Deep Learning with Differential Privacy

https://github.com/sunghoonim/DPSNet

DPSNet: End-to-end Deep Plane Sweep Stereo

https://github.com/sunhwan-lee/recommender_system

Deep Learning based Recommender System: A Survey and New Perspectives

https://github.com/sunyumark/ScaDec-deep-learning-diffractive-tomography

Efficient and accurate inversion of multiple scattering with deep learning

https://github.com/suryachintu/RSNA-Intracranial-Hemorrhage-Detection

Practical Window Setting Optimization for Medical Image Deep Learning

https://github.com/synthesio/vision-test

Scalable Deep Learning Logo Detection

https://github.com/tadorfer/NLProt

Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

https://github.com/taewankim1/uncertainty_deeplearning

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

https://github.com/takuseno/beta-vae

Early Visual Concept Learning with Unsupervised Deep Learning

https://github.com/takyamamoto/BRU_chainer

Deep learning improved by biological activation functions

https://github.com/takyamamoto/SNU_Chainer

Deep learning incorporating biologically-inspired neural dynamics

https://github.com/tangxyw/RecAlgorithm

Wide & Deep Learning for Recommender Systems

https://github.com/tankconcordia/deep_inv_opt

Deep Inverse Optimization

https://github.com/tanreinama/XceptionHourgrass---PyTorch

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/tanyanair/segmentation_uncertainty

Deep Bayesian Active Learning with Image Data

https://github.com/tanyuqian/learning-data-manipulation

Learning to Reweight Examples for Robust Deep Learning

https://github.com/tapankumarmishra/machine_learning

A guide to convolution arithmetic for deep learning

https://github.com/tarun-98/COMP8240_Project_GroupG

Facial Motion Prior Networks for Facial Expression Recognition

https://github.com/teddykoker/evidential-learning-pytorch

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/teja5832/horovod-elastic

Horovod: fast and easy distributed deep learning in TensorFlow

https://github.com/tensorflow/cleverhans

The Limitations of Deep Learning in Adversarial Settings

https://github.com/tensorflow/deepmath/tree/master/deepmath/deephol

HOList: An Environment for Machine Learning of Higher-Order Theorem Proving

https://github.com/tensorflow/models/tree/master/official/recommendation/ranking

Deep Learning Recommendation Model for Personalization and Recommendation Systems

https://github.com/tensorflow/models/tree/master/research/deeplab

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/tensorflow/models/tree/master/research/differential_privacy

Deep Learning with Differential Privacy

https://github.com/tensorflow/probability

TensorFlow Distributions

https://github.com/ternaus/angiodysplasia-segmentation

Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning

https://github.com/testingautomated-usi/surprise-adequacy

Guiding Deep Learning System Testing using Surprise Adequacy

https://github.com/tetrachrome/subpixel

A guide to convolution arithmetic for deep learning

https://github.com/thearod5/calorie-predictor

CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning

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