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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/siyi-wind/FairDisCo
공식 코드FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning
https://github.com/skokec/detectron-traffic-signs
공식 코드Deep Learning for Large-Scale Traffic-Sign Detection and Recognition
https://github.com/skokec/segdec-net-jim2019
공식 코드Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
https://github.com/skylineprof/skyline
공식 코드Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training
https://github.com/skywaLKer518/MultiplicativeMultimodal
공식 코드https://github.com/slds-lmu/seminar_multimodal_dl
공식 코드https://github.com/slerman12/ExplainingInteractions
공식 코드Explaining Local, Global, And Higher-Order Interactions In Deep Learning
https://github.com/sllambias/yucca
공식 코드https://github.com/slliuEric/WaveFuse_code
공식 코드WaveFuse: A Unified Deep Framework for Image Fusion with Discrete Wavelet Transform
https://github.com/smartjourneymining/sequential-deep-learning-models
공식 코드What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learning
https://github.com/snap-stanford/relbench
공식 코드RelBench: A Benchmark for Deep Learning on Relational Databases
https://github.com/snap-stanford/relbench-user-study
공식 코드RelBench: A Benchmark for Deep Learning on Relational Databases
https://github.com/snap-stanford/relgnn
공식 코드RelGNN: Composite Message Passing for Relational Deep Learning
https://github.com/snehjp2/agnet
공식 코드https://github.com/snu-list/blindharmony
공식 코드BlindHarmony: "Blind" Harmonization for MR Images via Flow model
https://github.com/snuspl/nimble
공식 코드Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
https://github.com/snwagh/falcon-public
공식 코드FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning
https://github.com/soeaver/awesome-human-parsing
공식 코드Deep Learning Technique for Human Parsing: A Survey and Outlook
https://github.com/soeaver/m2fp
공식 코드Deep Learning Technique for Human Parsing: A Survey and Outlook
https://github.com/softmatterlab/Quantitative-Microplankton-Tracker
공식 코드Microplankton life histories revealed by holographic microscopy and deep learning
https://github.com/softmatterlab/deep-learning-in-plankton-ecology
공식 코드https://github.com/softsys4ai/cl-robustness
공식 코드Rethinking Robust Contrastive Learning from the Adversarial Perspective
https://github.com/somaonishi/mtr
공식 코드Rethinking Data Augmentation for Tabular Data in Deep Learning
https://github.com/somepago/saint
공식 코드SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
https://github.com/songleee/venusvaccine
공식 코드Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection
https://github.com/sonic160/dtr_digital_model_simulink
공식 코드Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data
https://github.com/sophiali06/byteps_thc
공식 코드THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
https://github.com/sotelo/scribe
공식 코드A Robust Adaptive Stochastic Gradient Method for Deep Learning
https://github.com/soumyadeep-pal/diffeomorphic-image-registration-postprocess
공식 코드https://github.com/space-lab-sk/scss-net
공식 코드SCSS-Net: Solar Corona Structures Segmentation by Deep Learning
https://github.com/spcl/deep-weather
공식 코드Deep Learning for Post-Processing Ensemble Weather Forecasts
https://github.com/spcornelius/blue_zero
공식 코드https://github.com/spidy390/corrupting_labels_with_distillation
공식 코드https://github.com/spore1/cadex
공식 코드Explaining Deep Learning Models with Constrained Adversarial Examples
https://github.com/stanford-futuredata/selection-via-proxy
공식 코드Selection via Proxy: Efficient Data Selection for Deep Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.