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An Analysis of Unsupervised Pre-training in Light of Recent Advances
Tom Le Paine, Pooya Khorrami, Wei Han +1
Convolutional neural networks perform well on object recognition because of a number of recent advances: rectified linear units (ReLUs), data augmentation, dropout, and large label…
Decomposition-Based Domain Adaptation for Real-World Font Recognition
Zhangyang Wang, Jianchao Yang, Hailin Jin +4
We present a domain adaption framework to address a domain mismatch between synthetic training and real-world testing data. We demonstrate our method on a challenging fine-grain cl…
Scalable Similarity Learning using Large Margin Neighborhood Embedding
Zhaowen Wang, Jianchao Yang, Zhe Lin +3
Classifying large-scale image data into object categories is an important problem that has received increasing research attention. Given the huge amount of data, non-parametric app…