https://github.com/jindongwang/transferlearning
ftp://ftp.cs.wisc.edu/machine-learning/shavlik-group/torrey.handbook09.pdf
https://arxiv.org/pdf/1411.1792.pdf
https://cs231n.github.io/transfer-learning/
- CNN Features off-the-shelf: an Astounding Baseline for Recognition trains SVMs on features from ImageNet-pretrained ConvNet and reports several state of the art results.
- DeCAF reported similar findings in 2013. The framework in this paper (DeCAF) was a Python-based precursor to the C++ Caffe library.
- How transferable are features in deep neural networks? studies the transfer learning performance in detail, including some unintuitive findings about layer co-adaptations.
Books
Papers
- A survey on transfer learning, 2010.
- Chapter 11: Transfer Learning, Handbook of Research on Machine Learning Applications, 2009.
- How transferable are features in deep neural networks?
Pre-trained Models
- Oxford VGG Model
- Google Inception Model
- Microsoft ResNet Model
- Google’s word2vec Model
- Stanford’s GloVe Model
- Caffe Model Zoo