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In this paper, we propose a topographic subspace learning algorithm, named key-coding learning, which utilizes irrelevant unlabeled auxiliary data to facilitate image classification and retrieval tasks. It is worth noticing that we do not need to assume the auxiliary data follows the same class labels or generative distribution as the target training data. Firstly, the subspace model is learnt from...
In the setting of active learning there exists a general assumption that labeled examples are available for training a classifier, which in turn is used to examine unlabeled data to select the most ‘informative’ examples for manual labeling. However, in some domain applications there are a limited number of labeled examples available, such as in the most extreme cases of having a single labeled example...
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