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Deep Learning from Temporal Coherence in Video
This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
