https://github.com/taki0112/DRIT-Tensorflow
Diverse Image-to-Image Translation via Disentangled Representations
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
Diverse Image-to-Image Translation via Disentangled Representations
Very Deep Convolutional Networks for Large-Scale Image Recognition
Learning to Adapt Structured Output Space for Semantic Segmentation
HLT-NUS SUBMISSION FOR 2020 NIST Conversational Telephone Speech SRE
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Joint Discriminative and Generative Learning for Person Re-identification
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
Very Deep Convolutional Networks for Large-Scale Image Recognition
Visual Representations for Semantic Target Driven Navigation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
Very Deep Convolutional Networks for Large-Scale Image Recognition
Regressive Domain Adaptation for Unsupervised Keypoint Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.