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deep-transfer-learning's Introduction

Deep Transfer Learning


When Transfer Learning Meets Deep Learning

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New Trends

  • GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations

  • Learning to Transfer (ICML-18)

  • Label Efficient Learning of Transferable Representations across Domains and Tasks (NIPS-17)

Unbalanced Transfer

  • Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation (CVPR-18)

  • Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation (AAAI-18)

  • Importance Weighted Adversarial Nets for Partial Domain Adaptation (CVPR-18)

  • Partial Transfer Learning with Selective Adversarial Networks (CVPR-18)

  • Partial Adversarial Domain Adaptation (ECCV-18)


Distilling Aproach

  • Distilling the Knowledge in a Neural Network (NIPS-14)

  • Born Again Neural Networks (ICML-18)

  • NITE : A Neural Inductive Teaching Framework for Domain-Specific NER (EMNLP-17)


Target Data: labelled, Source Data: labelled

  • DAN/JAN (Deep Adaptation Network/Joint Adaptation Network, ICML-15,17)
  • Learning Multiple Tasks with Multilinear Relationship Networks (NIPS-17)
  • Multi-Adversarial Domain Adaptation (AAAI-18)
  • Partial Transfer Learning with Selective Adversarial Networks (arXiv-17)
  • Gradient Episodic Memory for Continual Learning (NIPS-17)
  • Unified deep supervised domain adaptation and generalization (ICCV-17)
  • Semi-supervised learning knowledge transfer for deep learning from private training data (ICLR-17)
  • Net2Net: Accelerating Learning via Knowledge Transfer(ICLR-16)

Evolution based

  • Overcoming Catastrophic Forgetting in Neural Networks (PNAS-17)
  • Progressive Neural Networks (arXiv-16)
  • Evolution Channels Gradient Descent in Super Neural Networks (arXiv-17)
  • PathNet: Evolution Channels Gradient Descent in Super Neural Networks (arxiv-17)

One-shot learning

  • One-shot Learning with Memory-Augmented Neural Networks (arXiv-16)
  • Siamese Neural Networks for One-Shot Image Recognition (ICML-15)
  • Learning to Compare: Relation Network for Few-Shot Learning (arXiv-17)

Target Data: labelled, Source Data: unlabelled

Self Taught Learning


Target Data: unlabelled, Source Data: labelled

  • RTN (Unsupervised Domain Adaptation with Residual Transfer Networks, NIPS-16)
  • Associative Domain Adaptation (ICCV-17)
  • Deep CORAL: Correlation Alignment for Deep Domain Adaptation (ECCV-16)
  • Domain Separation Networks (NIPS-16)
  • Deep Hashing Network for Unsupervised Domain Adaptation (CVPR-17)
  • Deep Transfer Network: Unsupervised Domain Adaptation (arXiv-16)
  • Joint distribution optimal transportation for domain adaptation (NIPS-17)
  • When Unsupervised Domain Adaptation Meets Tensor Representations (ICCV-17)
  • Self-ensembling for visual domain adaptation (ICLR-18)
  • AutoDIAL: Automatic DomaIn Alignment Layers (ICCV-17)
  • Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation (ICLR-18)
  • Asymmetric Tri-training for Unsupervised Domain Adaptation (arXiv-17)
GAN based
  • Learning Semantic Representations for Unsupervised Domain Adaptation (ICML-18)

  • Unsupervised Domain Adaptation by Backpropagation (ICML-15)

  • Domain-Adversarial Training of Neural Networks (JMLR-16)

  • Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks (CVPR-17)

  • ADDA (Adversarial Discriminative Domain Adaptation, arXiv-17)

  • Coupled Generative Adversarial Networks (NIPS-16)

  • Wasserstein Distance Guided Representation Learning for Domain Adaptation (AAAI-18)

  • Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery (CVPR-18)

RNN based
  • Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks (ICLR-17)

Zero-shot learning

Target Data: unlabelled, Source Data: unlabelled

Self Taught Clustering

  • Self-Taught Convolutional Neural Networks for Short Text Clustering (Neural Networks-17)

Concept Drift

  • Dynamic Weighted Majority for Incremental Learning of Imbalanced Data Streams with Concept Drift (IJCAI-17)

Transferability Analysis

  • How transferable are features in deep neural networks?

Transfer Learning for NLP

  • Adversarial Multi-task Learning for Text Classification (ACL-17)

  • Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks (IJCAI-18)

  • Cross-Domain Sentiment Classification with Target Domain Specific Information (ACL-18)

  • Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (ACL-18)

  • Universal Language Model Fine-tuning for Text Classificatione (ACL-18)

  • Improving Language Understanding by Generative Pre-Training

  • Universal Sentence Encoder


Appliactions

  • TransNets: Learning to Transform for Recommendation (RecSys-17)
  • Empower Sequence Labeling with Task-Aware Neural Language Model (AAAI-18)
  • Adversarial Learning For Semi-Supervised Semantic Segmentation (ICLR 2018)
  • Cross-Domain Sentiment Classification via Topic-Related TrAdaBoost (AAAI-17)

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