https://github.com/jabalazs/repeval_rivercorners
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
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/jacobgil/vit-explain
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/jacobtyo/mudd
Learning Generalisable Omni-Scale Representations for Person Re-Identification
https://github.com/jaketae/mlp-mixer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/james77777778/keras-image-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jameswang287/Car-Detection
https://github.com/jameszhou-gl/cbre
Cycle-Balanced Representation Learning For Counterfactual Inference
https://github.com/janZub-AI/EmotionDetectionVGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/janfreyberg/pytorch-revgrad
https://github.com/jankrepl/mildlyoverfitted
https://github.com/jason718/game-feature-learning
Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery
https://github.com/jason90330/EdgeFinal
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/jayroxis/ordering-in-graph-generation
https://github.com/jbogensperger/DRUG_CROSSNER
https://github.com/jdauba/Domain-Adaptation-SVHN-MNIST
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
https://github.com/jday96314/Kuzushiji
https://github.com/jeonsworld/ViT-pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/jeremyzhao1998/mrt-release
https://github.com/jessica3724/Keras_VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jessiemjones/TF-2.0-Neural-Style-Transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jetpacapp/DeepBeliefSDK
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/jhonve/implicitpcda
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
https://github.com/jiajunhua/facebookresearch-Detectron
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jiangsikai/Person_reID_baseline_pytorch
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/jiangtaoxie/So-ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/jiangxingxun/Seeking_Salient_Facial_Region
Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition
https://github.com/jianshen92/stanford-car-grab-challenge
https://github.com/jiequancui/Parametric-Contrastive-Learning
https://github.com/jihaoxuanye/MetaPRD
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop
https://github.com/jinlong17/advgps
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
https://github.com/jis478/Tensorflow/tree/master/TF2.0/Cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/jis478/cutmix_tensorflow2
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/jiweibo/imagenet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jizongFox/ReproduceAdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/jjessyli/speciteller
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.