Best Practices for Fine-Tuning Visual Classifiers to New Domains

Author:

Chu Brian,Madhavan Vashisht,Beijbom Oscar,Hoffman Judy,Darrell Trevor

Publisher

Springer International Publishing

Reference20 articles.

1. Lecture Notes in Computer Science;MD Zeiler,2014

2. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: International Conference in Machine Learning (ICML) (2014)

3. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. (2007)

4. Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Technical report 7694, California Institute of Technology (2007)

5. Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)

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