https://github.com/lewis-morris/image_augment
Improved Regularization of Convolutional Neural Networks with Cutout
Papers with Code 아카이브
Domain Adaptation
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Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lfhase/feat
https://github.com/lh-wang/ACC
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/lianqing11/pycda
https://github.com/libreai/neural-painters-x
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lightly-ai/lightly
https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION
https://github.com/linear95/tc-estimation
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
https://github.com/lingxiaoyuan/ood_mechanics
https://github.com/linhduongtuan/Fruits_Vegetables_Classifier_WebApp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/links-ads/igarss-multi-temporal-hotspot-detection
Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery
https://github.com/links-ads/spada
Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery
https://github.com/linweizhedragon/knowledge-aware-graph-enhanced-gpt-2-for-dialogue-state-tracking
Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
https://github.com/lit-leo/inner-convex-nn
https://github.com/lithium770/Unsupervised-Person-Re-identification-with-Stochastic-Training-Strategy
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/lithium770/unsupervised-person-re-id-with-stochastic-training-strategy
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/litian96/fair_flearn
https://github.com/liuch37/image-processing
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/liujiyuan13/MAE-code
https://github.com/liuruiyang98/Jittor-MLP
https://github.com/liuwei16/ALFNet
https://github.com/liuxingwt/CLS
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/liuyao12/ConvNets-PDE-perspective
https://github.com/liuyugeng/baadd
https://github.com/lixihan/Resnet
https://github.com/lixixibj/forecasting-with-time-series-imaging
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lixixibj/tsImage-forecasting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ljy-hy/mentormix_pytorch
https://github.com/lkeonwoo94/DL_cv-mdt_NVIDIA_Cert_Course_StudyPI
https://github.com/lnstadrum/fastaugment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/locuslab/fast_adversarial
https://github.com/lofrienger/onetomany_toolsynseg
Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need
https://github.com/longhao-chen/aicas2024
https://github.com/lonnyzhang423/github-hot-hub
https://github.com/lornatang/googlenet-pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lpirola13/flower-recognizer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.