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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/uabinf/nlp-fall-2019-project-image_captioning

A Comprehensive Survey of Deep Learning for Image Captioning

https://github.com/ubamba98/Brain-Segmentation

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

https://github.com/ubc-tea/Improving-Fairness-in-Image-Classification-via-Sketching

Estimating and Improving Fairness with Adversarial Learning

https://github.com/ucb-bar/chipyard

Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration

https://github.com/ucsb-nlp-chang/textgrad

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/udion/DeepUncer

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

https://github.com/ulaval-damas/Tree-Bark-Re-Identification

Tree bark re-identification using a deep-learning feature descriptor

https://github.com/ulissigroup/uncertainty_benchmarking

Accurate Uncertainties for Deep Learning Using Calibrated Regression

https://github.com/umitkacar/ai-edge-computing

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

https://github.com/umutguneri/Question-Answering-Assistant

Deep Learning for Answer Sentence Selection

https://github.com/universvm/BacXeption

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/uw-biomedical-ml/hvfProgression

Forecasting Future Humphrey Visual Fields Using Deep Learning

https://github.com/val-iisc/flss

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/vale-salvatelli/sdo-autocal_pub

Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning

https://github.com/valentinchelle/kerasOnSpark

Deep learning with Elastic Averaging SGD

https://github.com/vasusingla/low_curvature_activations

Overfitting in adversarially robust deep learning

https://github.com/victor369basu/ProteinStructurePrediction

Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

https://github.com/victordibia/deepad

Deep Learning for Anomaly Detection: A Survey

https://github.com/vidits-kth/py-radio-autoencoder

An Introduction to Deep Learning for the Physical Layer

https://github.com/vijaylaxmid/DeepLearning_3DPointClouds

Deep Learning for 3D Point Clouds: A Survey

https://github.com/vinayakumarr/maching-learning-CDAC-Technopark

DeepImageSpam: Deep Learning based Image Spam Detection

https://github.com/vinaymittal22/Income_Prediction_US

Wide & Deep Learning for Recommender Systems

https://github.com/vinits5/learning3d

RPM-Net: Robust Point Matching using Learned Features

https://github.com/vinits5/masknet

RPM-Net: Robust Point Matching using Learned Features

https://github.com/vinniesun/snntorch-ipu

Training Spiking Neural Networks Using Lessons From Deep Learning

https://github.com/violin0847/crowdcounting

CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

https://github.com/visionxlab/lrs-vqa

FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery

https://github.com/vislearn/DSACLine

DSAC - Differentiable RANSAC for Camera Localization

https://github.com/vislearn/dsacstar

DSAC - Differentiable RANSAC for Camera Localization

https://github.com/vislearn/ngdsac_camreloc

DSAC - Differentiable RANSAC for Camera Localization

https://github.com/vita-epfl/trajnetplusplusbaselines

Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

https://github.com/vlimant/NNLO

Deep learning with Elastic Averaging SGD

https://github.com/vlimant/mpi_learn

Deep learning with Elastic Averaging SGD

https://github.com/vlislab22/360_Survey

Deep Learning for Omnidirectional Vision: A Survey and New Perspectives

https://github.com/vohoaiviet/indoor_localization

Low-effort place recognition with WiFi fingerprints using deep learning

https://github.com/voxelmorph/voxelmorph

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation

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