https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
Very Deep Convolutional Networks for Large-Scale Image Recognition
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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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis
Beyond Normal: On the Evaluation of Mutual Information Estimators
Very Deep Convolutional Networks for Large-Scale Image Recognition
Aggregated Residual Transformations for Deep Neural Networks
Simulating Content Consistent Vehicle Datasets with Attribute Descent
Joint Discriminative and Generative Learning for Person Re-identification
GenGMM: Generalized Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation
Unsupervised Multi-Source Domain Adaptation for Person Re-Identification
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
Learning to Adapt Structured Output Space for Semantic Segmentation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Guided Alignment Training for Topic-Aware Neural Machine Translation
Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation
COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.