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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/fuhengzhou/lsnet

공식 코드

A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior

https://github.com/furkath/dl_framework-for-pbs-cloth-simulation

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A Physics-embedded Deep Learning Framework for Cloth Simulation

https://github.com/fursovia/adversarial_sber

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Adversarial Attacks on Deep Models for Financial Transaction Records

https://github.com/fuzhenxin/Style-Transfer-in-Text

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Deep Learning for Text Style Transfer: A Survey

https://github.com/gabriele-dominici/causalcgm

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Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning

https://github.com/gabrielkasmi/robust_pv_mapping

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Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systems

https://github.com/gabrielmittag/NISQA

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Deep Learning Based Assessment of Synthetic Speech Naturalness

https://github.com/galoisinc/dlkoopman

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DLKoopman: A deep learning software package for Koopman theory

https://github.com/gama-ufsc/brain-age

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Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers?

https://github.com/ganggit/jointmodel

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Joint Visual Denoising and Classification using Deep Learning

https://github.com/gdetor/differential_equations_dnn

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Practical Aspects on Solving Differential Equations Using Deep Learning: A Primer

https://github.com/geekJZY/arcticnet

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ArcticNet: A Deep Learning Solution to Classify Arctic Wetlands

https://github.com/genandlam/multi-modal-depression-detection

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Context-Aware Deep Learning for Multi Modal Depression Detection

https://github.com/geods/region2vec-gat

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GeoAI-Enhanced Community Detection on Spatial Networks with Graph Deep Learning

https://github.com/geohai/sea-ice-segment

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Deep Learning on SAR Imagery: Transfer Learning Versus Randomly Initialized Weights

https://github.com/geometric-intelligence/TopoBench

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TopoBench: A Framework for Benchmarking Topological Deep Learning

https://github.com/georgekillick90/fovconvnext

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Foveation in the Era of Deep Learning

https://github.com/gewelsji/vps

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Video Polyp Segmentation: A Deep Learning Perspective

https://github.com/ghliu/mean-field-fcdnn

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Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective

https://github.com/gianlucarloni/crocodile

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CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning

https://github.com/gift-surg/MONAIfbs

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MONAIfbs: MONAI-based fetal brain MRI deep learning segmentation

https://github.com/gillesvandevyver/arqee

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Regional quality estimation for echocardiography using deep learning

https://github.com/gist-ailab/Food-Instance-Segmentation

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Deep Learning based Food Instance Segmentation using Synthetic Data

https://github.com/giu-guarino/r-pnn

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Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation

https://github.com/giuliotosato/autokeras-bioacustic

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Auto deep learning for bioacoustic signals

https://github.com/givkashi/cair

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Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions

https://github.com/gknilsen/pydeepdelta

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Epistemic Uncertainty Quantification in Deep Learning Classification by the Delta Method

https://github.com/gknilsen/pyhessian

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Efficient Computation of Hessian Matrices in TensorFlow

https://github.com/gmum/zeus

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ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data

https://github.com/gnina/libmolgrid

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libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

https://github.com/goerigk/RO-DNN

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Data-Driven Robust Optimization using Unsupervised Deep Learning

https://github.com/goldblum/TruthOrBackpropaganda

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Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory

https://github.com/goodboyanush/dl-model-recommend

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"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets

https://github.com/google-research/DP-FTRL

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Practical and Private (Deep) Learning without Sampling or Shuffling

https://github.com/google-research/e3x

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E3x: $\mathrm{E}(3)$-Equivariant Deep Learning Made Easy

https://github.com/google-research/federated

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Practical and Private (Deep) Learning without Sampling or Shuffling

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