Fetching the paper…
Reading the bibliography…
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.
Lifelong robot learning
Thrun, S. and Mitchell, T. M · 1995
Earlier work this paper cites.
Theory of point estimation
Lehmann, E. L. and Casella, G · 1998
Earlier work this paper cites.
Adaptation of maximum entropy capitalizer: Little data can help a lot
Chelba, C. and Acero, A · 2006
Earlier work this paper cites.
One-shot learning of object categories
Fei-Fei, L., Fergus, R., and Perona, P · 2006
Earlier work this paper cites.
Caltech-256 object category dataset
Griffin, G., Holub, A., and Perona, P · 2007
Earlier work this paper cites.
Adapting svm classifiers to data with shifted distributions
Yang, J., Yan, R., and Hauptmann, A. G · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Efficient online and batch learning using forward backward splitting
Duchi, J. and Singer, Y · 2009
Earlier work this paper cites.
Recognizing indoor scenes
Quattoni, A. and Torralba, A · 2009
Earlier work this paper cites.
The Pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A · 2010
Earlier work this paper cites.
A survey on transfer learning
Pan, S. J. and Yang, Q · 2010
Earlier work this paper cites.
Tabula rasa: Model transfer for object category detection
Aytar, Y. and Zisserman, A · 2011
Earlier work this paper cites.
Novel dataset for fine-grained image categorization: Stanford dogs
Khosla, A., Jayadevaprakash, N., Yao, B., and Li, F.-F · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Speaker adaptation of context dependent deep neural networks
Liao, H · 2013
Cited alongside, same era.
Food-101 – mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Van Gool, L · 2014
Cited alongside, same era.
Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
Cited alongside, same era.
Speaker adaptive training using deep neural networks
Ochiai, T., Matsuda, S., Lu, X., Hori, C., and Katagiri, S · 2014
Cited alongside, same era.
Learning categories from few examples with multi model knowledge transfer
Tommasi, T., Orabona, F., and Caputo, B · 2014
Wide-slice residual networks for food recognition
Martinel, N., Foresti, G. L., and Micheloni, C · 2016
Later among the works it cites.
You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A · 2016
Later among the works it cites.
Beyond sharing weights for deep domain adaptation
Rozantsev, A., Salzmann, M., and Fua, P · 2016
Later among the works it cites.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Later among the works it cites.
Regularization techniques for fine-tuning in neural machine translation
Barone, A. V. M., Haddow, B., Germann, U., and Sennrich, R · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
Cited alongside, same era.
Lifelong learning with non-iid tasks
Pentina, A. and Lampert, C. H · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L · 2017
Later among the works it cites.
Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning
Ge, W. and Yu, Y · 2017
Later among the works it cites.
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A · 2017
Later among the works it cites.
Strategies for conceptual change in convolutional neural networks
Grachten, M. and Chacón, C. E. C · 2017
Later among the works it cites.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
Later among the works it cites.
Learning without forgetting
Li, Z. and Hoiem, D · 2017
Later among the works it cites.
Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J · 2017
Later among the works it cites.
Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
Later among the works it cites.