https://github.com/MilesCranmer/symbolic_deep_learning
공식 코드Discovering Symbolic Models from Deep Learning with Inductive Biases
Papers with Code 아카이브
Deep Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Mvrjustid/ACII19-Apex-Time-Network
공식 코드A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
https://github.com/MyCaffe/MyCaffe
공식 코드MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning
https://github.com/ND-HowardGroup/SPIE-CNN-FLIM-Denoising
공식 코드Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)
https://github.com/ND-HowardGroup/SPIE-CNN-SR
공식 코드Deep learning-based super-resolution fluorescence microscopy on small datasets
https://github.com/NIPS2019-authors/MoLe_public
공식 코드Towards Efficient and Secure Delivery of Data for Training and Inference with Privacy-Preserving
https://github.com/NVIDIA/TensorRT
공식 코드EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis
https://github.com/NVIDIAGameWorks/kaolin
공식 코드Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
https://github.com/NVlabs/nvTorchCam
공식 코드nvTorchCam: An Open-source Library for Camera-Agnostic Differentiable Geometric Vision
https://github.com/NYUMedML/DARTS
공식 코드DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation
https://github.com/Nathan-UW/OpenGF
공식 코드https://github.com/NeuralClassifier/Concept-Complexity-n-Imbalance-DL
공식 코드On the combined effect of class imbalance and concept complexity in deep learning
https://github.com/NeuromorphicComputing/SoftHebb
공식 코드https://github.com/NiWaRe/DPBenchmark
공식 코드SmoothNets: Optimizing CNN architecture design for differentially private deep learning
https://github.com/NifTK/NiftyNet
공식 코드https://github.com/NilsBochow/lama_reconstruction
공식 코드https://github.com/Nixtla/nixtla
공식 코드https://github.com/OATML/Non-Parametric-Transformers
공식 코드Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
https://github.com/OmarMohammed88/AR-Emotion-Recognition
공식 코드Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED Dataset
https://github.com/Oneflow-Inc/oneflow
공식 코드OneFlow: Redesign the Distributed Deep Learning Framework from Scratch
https://github.com/OniDaito/Holly
공식 코드https://github.com/Optimization-AI/LibAUC
공식 코드https://github.com/PKU-IMRE/Retina
공식 코드Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
https://github.com/PaddlePaddle/InterpretDL
공식 코드Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond
https://github.com/PaddlePaddle/Paddle
공식 코드https://github.com/PengBoXiangShang/torchsketch
공식 코드https://github.com/PengyiZhang/MIADeepSSL
공식 코드A Survey on Deep Learning of Small Sample in Biomedical Image Analysis
https://github.com/PhIMaL/DeePyMoD
공식 코드https://github.com/Plazzzzma/CP_project_2020
공식 코드https://github.com/Pradeepiit/hf0
공식 코드https://github.com/PredictiveIntelligenceLab/DeepStefan
공식 코드https://github.com/Project-MONAI/MONAI
공식 코드MONAI: An open-source framework for deep learning in healthcare
https://github.com/ProminentLab/DREAM-NAP
공식 코드https://github.com/Puckett-Lab/explainability_CorticalSurfaceGDL
공식 코드An explainability framework for cortical surface-based deep learning
https://github.com/PyRetri/PyRetri
공식 코드https://github.com/QData/AdversarialDNN-Playground
공식 코드Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.