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The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent… Expand Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design… Expand We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto… Expand Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state-of-the… Expand Transfer learning is established as an effective technology in computer vision for leveraging rich labeled data in the source… Expand Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging… Expand A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made… Expand Abstract
We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of… Expand Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more… Expand We present a method for learning a low-dimensional representation which is shared across a set of multiple related tasks. The… Expand