https://github.com/akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101
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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Improved Regularization of Convolutional Neural Networks with Cutout
Very Deep Convolutional Networks for Large-Scale Image Recognition
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
Virtual to Real Reinforcement Learning for Autonomous Driving
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
Discrete Representations Strengthen Vision Transformer Robustness
HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.