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Taking the 16-layer VGGNet trained under the Image Net ILSVRC 2012 protocol as a strong baseline for image classification, our methods improve the validation-set accuracy by a noticeable margin.
In addition to identifying the content within a single image, relating images and generating related images are critical tasks for image understanding.
To better address natural-language-based visual entity localization, we propose a discriminative approach.
We formulate a discriminative bimodal neural network (DBNet), which can be trained by a classifier with extensive use of negative samples.
Inspired by the recent trend toward revisiting the importance of unsupervised learning, we investigate joint supervised and unsupervised learning in a large-scale setting by augmenting existing neural networks with decoding pathways for reconstruction.