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DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
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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.
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
Very Deep Convolutional Networks for Large-Scale Image Recognition
Domain Adaptation for Object Detection via Style Consistency
Very Deep Convolutional Networks for Large-Scale Image Recognition
3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views
Style Augmentation: Data Augmentation via Style Randomization
Very Deep Convolutional Networks for Large-Scale Image Recognition
Multi-Task Deep Neural Networks for Natural Language Understanding
Multi-Task Deep Neural Networks for Natural Language Understanding
Unsupervised Domain Adaptation via Calibrating Uncertainties
SuperPoint: Self-Supervised Interest Point Detection and Description
LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Cross-Lingual Adaptation using Structural Correspondence Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.