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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/SalemAmeen/raspberry_pi_optimization

공식 코드

Optimizing Deep Learning Models For Raspberry Pi

https://github.com/Sanghyun-Hong/How-to-0wn-NAS-in-Your-Spare-Time

공식 코드

How to 0wn NAS in Your Spare Time

https://github.com/SankhaSubhra/GAMO

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Generative Adversarial Minority Oversampling

https://github.com/SensiLab/Andy-Lomas-Generative-Art-Dataset

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Deep Learning of Individual Aesthetics

https://github.com/Shakarim94/Net-SURE

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Training Deep Learning Based Denoisers without Ground Truth Data

https://github.com/ShaneShen/ENL-FCN

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Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification

https://github.com/ShashankSule/ILR-for-MWF

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Input layer regularization and automated regularization hyperparameter tuning for myelin water estimation using deep learning

https://github.com/Shawn-Shan/fawkes

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Fawkes: Protecting Privacy against Unauthorized Deep Learning Models

https://github.com/ShinyCode/image-outpainting

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Painting Outside the Box: Image Outpainting with GANs

https://github.com/SihamTabik/Pistol-Detection-in-Videos

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Automatic Handgun Detection Alarm in Videos Using Deep Learning

https://github.com/SirRob1997/Descending-through-a-Crowded-Valley---Results

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Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers

https://github.com/SiriusHou/DLRS_CVR_BAT-Deep-learning-enabled-brain-hemodynamic-mapping-using-resting-state-fMRI

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Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI

https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs

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A Temporal Kernel Approach for Deep Learning with Continuous-time Information

https://github.com/Sushil-Thapa/Abstention-OoD

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An Effective Baseline for Robustness to Distributional Shift

https://github.com/SySeVR/SySeVR

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SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities

https://github.com/SymbioticLab/Salus

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Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

https://github.com/Synho/windows_optimization

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Practical Window Setting Optimization for Medical Image Deep Learning

https://github.com/THUDM/cogdl

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CogDL: A Comprehensive Library for Graph Deep Learning

https://github.com/THUYimingLi/backdoor-learning-resources

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Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review

https://github.com/TUM-AIMED/PrivateSegmentation

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Differentially private federated deep learning for multi-site medical image segmentation

https://github.com/TUMLegalTech/bva-citation-prediction

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Context-Aware Legal Citation Recommendation using Deep Learning

https://github.com/TaeseongYoon/DAEDL

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Uncertainty Estimation by Density Aware Evidential Deep Learning

https://github.com/TaraEsmaeilbeig/one_bit_DoA_estimation

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Deep Learning-Enabled One-Bit DoA Estimation

https://github.com/TeCSAR-UNCC/Deep_RACE

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Real-time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters

https://github.com/Testing-Multiple-DL-Models/SDS

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Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models

https://github.com/ThomasMiconi/HebbianCNNPyTorch

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Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworks

https://github.com/TongtongFANG/DIW

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Rethinking Importance Weighting for Deep Learning under Distribution Shift

https://github.com/TovlyDeutsch/Linguistic-Features-for-Readability

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Linguistic Features for Readability Assessment

https://github.com/UBC-NLP/aranet

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AraNet: A Deep Learning Toolkit for Arabic Social Media

https://github.com/UCLA-StarAI/Semantic-Loss

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A Semantic Loss Function for Deep Learning with Symbolic Knowledge

https://github.com/UK-Digital-Heart-Project/4Dsurvival

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Deep learning cardiac motion analysis for human survival prediction

https://github.com/USC-Melady/Benchmarking_DL_MIMICIII

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Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets

https://github.com/VADERASU/visual-analytics-for-deep-transfer-learning

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A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes

https://github.com/VIPriors/vipriors-challenges-toolkit

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VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

https://github.com/VLISLAB/360-DL-Survey

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Deep Learning for Omnidirectional Vision: A Survey and New Perspectives

https://github.com/Vahid-Negahdari/Elastic-Full-Waveforrm-Inversion

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Integrating Physics of the Problem into Data-Driven Methods to Enhance Elastic Full-Waveform Inversion with Uncertainty Quantification

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