Fetching the paper…
Reading the bibliography…
We present a novel approach to leverage large unlabeled datasets by pre-training state-of-the-art deep neural networks on randomly-labeled datasets.
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y.-W · 2006
Earlier work this paper cites.
Greedy Layer-Wise Training of Deep Networks
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H · 2007
Earlier work this paper cites.
The difficulty of training deep architectures and the effect of unsupervised pre-training
Erhan, D., Manzagol, P.-A., Bengio, Y., Bengio, S., and Vincent, P · 2009
Earlier work this paper cites.
Data clustering: 50 years beyond K-means
Jain, A. K · 2009
Earlier work this paper cites.
Why does unsupervised pre-training help deep learning?
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.-A., Vincent, P., and Bengio, S · 2010
Earlier work this paper cites.
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., and Serre, T · 2011
Earlier work this paper cites.
Stacked convolutional auto-encoders for hierarchical feature extraction
Masci, J., Meier, U., Cireşan, D., and Schmidhuber, J · 2011
Earlier work this paper cites.
Autoencoders, Unsupervised Learning, and Deep Architectures
Baldi, P · 2012
Earlier work this paper cites.
3D Convolutional neural networks for human action recognition
Ji, S., Xu, W., Yang, M., and Yu, K · 2012
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., and Shah, M · 2012
Cited alongside, same era.
Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
Cited alongside, same era.
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T · 2014
Cited alongside, same era.
Large-scale video classification with convolutional neural networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Li, F. F · 2014
Cited alongside, same era.
Statistics - youtube, 2014
Statistics, Y · 2014
Cited alongside, same era.
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Unsupervised extraction of video highlights via robust recurrent auto-encoders
Yang, H., Wang, B., Lin, S., Wipf, D., Guo, M., and Guo, B · 2015
Later among the works it cites.
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2015
Later among the works it cites.
Stacked What-Where Auto-encoders
Zhao, J., Mathieu, M., Goroshin, R., and LeCun, Y · 2015
Later among the works it cites.
Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2016
Later among the works it cites.
A Pitfall of Unsupervised Pre-Training
Alberti, M., Seuret, M., Ingold, R., and Liwicki, M · 2017
Later among the works it cites.
The kinetics human action video dataset
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., and Brox, T · 2015
Cited alongside, same era.
Convolutional clustering for unsupervised learning
Dundar, A., Jin, J., and Culurciello, E · 2015
Cited alongside, same era.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Radford, A., Metz, L., and Chintala, S · 2015
Cited alongside, same era.
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M · 2015
Cited alongside, same era.
Later among the works it cites.
Are You Tampering With My Data?
Alberti, M., Pondenkandath, V., Würsch, M., Bouillon, M., Seuret, M., Ingold, R., and Liwicki, M · 2018
Closest in time.
Measuring the Intrinsic Dimension of Objective Landscapes
Li, C., Farkhoor, H., Liu, R., and Yosinski, J · 2018
Closest in time.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2059
Closest in time.