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Experiments demonstrate that our CNN tracker outperforms the state-of-the-art methods on three recent benchmarks (over 80 video sequences), which illustrates the superiority of the feature representations learned by our purely online bagging framework.

In chapter 3, we investigate the transfer of representations learned by kernel methods employed by support vector machines to linear regression models and characterize the tradeoffs in classification performance and computation time.

In this talk I will describe how multilingual speech representations learned from a variety of languages can reduce the amount of data needed to train a speech recognition system in a new language.

Here, the goal is to classify images of 3965 vessels in IMO testing set by the use of generic vessel representations learned on images of IMO training set.

Here, the deep representations learned specifically for maritime vessels significantly outperform the deep representation (VGG-F) learned for general object categorization for 1000 classes [2, 20] for both distance metrics.

Table 2 Vessel recognition performance on IMO testing set, composed of 3965 marine vessels, by utilizing nearest neighbor search on 109-, 4035-, and 4144-dimensional output-based representations learned in IMO training set   109-dim.

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Figure 3a, b shows the representations learnt with traditional exponential STDP learning25 against the adaptive plasticity-based learning.

Low-level features capture the time and frequency domain property while mid-level representations learn the composition of the action.

In this paper, deep self-taught learning is utilized to obtain hierarchical representations, learn the concept of facial beauty and produce human-like predictor.

The representations learnt on the last layer can be used for different tasks.

To understand how this network is performing so well, we studied the representation learned by the network.

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