Unsupervised and transfer learning challenge

35 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Pylearn2: a machine learning research library (2013, methodology): “These include the datasets used for DARPA’s unsupervised and transfer learning challenge [25], the dataset used for the NIPS 2011 workshops challenge in representation learning [32], the CIFAR-10 and CIFAR-100 datasets [29], the MNIST dataset [34], some of the MNIST variations datasets [31], the…”
Challenges in representation learning: A report on three machine learning contests (2013, neutral): “This idea was similar to a recent DARPA-organized unsupervised and transfer learning challenge [2] which used obfuscated data and required submission of a representation of the data that would then be used on the competition server to train a very weak classiﬁer.”
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning (2017, background): “, 2010) to mitigate this phenomenon, including training autoencoders to shift the distribution of the synthetic data to the distribution of the real data (Guyon et al., 2011; Zhang et al., 2015).”
Foundations and Advances in Deep Learning (2014, background): “It has been noticed by many researchers, for instance Salakhutdinov and Hinton (2009a) and Desjardins et al.”