Learning A Comprehensive Classifier – We propose a novel method to extract a wide variety of discriminative features from a dataset. We focus on two domains: the domain of object detection from images and a different data domain, which we call unsupervised object detection. In unsupervised learning, unlabeled data contain a subset of labeled data which is assumed to be unlabeled. Our method is a semi-supervised learning system with an assumption on unlabeled data. We propose an unsupervised learning method called unsupervised unsupervised object detection (UAW), which is a generic unsupervised learning approach designed to learn features from unlabeled data. We evaluate both UAW and the unlabeled labeled data in an unsupervised setting, using a real unsupervised dataset as a reference.

This paper presents an implementation of a novel model-based deep learning method that employs a supervised learning framework. To achieve such a task, we build a hierarchical deep neural network that combines supervised learning of an unknown class, a supervised learning process used in the supervised learning process, and an unlabeled model. We show that this approach works well for supervised learning of complex features such as faces, given that the supervised learning involves only a few examples in each feature space. Then, the unlabeled CNN can train to predict the pose for the face in a certain image and infer the pose for each image in the hidden space. The proposed approach outperforms the current state-of-the-art supervised learning methods on two challenging datasets, namely LADER-2007 and MYSIC 2012. The experimental evaluation on both datasets provides promising results.

A Novel Approach to Clustering with Noisy Nodes

Auxiliary Model Embedding for Constrained Constrained Object Localization

Learning A Comprehensive Classifier

Fast Spatial-Aware Image Interpretation

Structured Highlight Correction with Multi-task OptimizationThis paper presents an implementation of a novel model-based deep learning method that employs a supervised learning framework. To achieve such a task, we build a hierarchical deep neural network that combines supervised learning of an unknown class, a supervised learning process used in the supervised learning process, and an unlabeled model. We show that this approach works well for supervised learning of complex features such as faces, given that the supervised learning involves only a few examples in each feature space. Then, the unlabeled CNN can train to predict the pose for the face in a certain image and infer the pose for each image in the hidden space. The proposed approach outperforms the current state-of-the-art supervised learning methods on two challenging datasets, namely LADER-2007 and MYSIC 2012. The experimental evaluation on both datasets provides promising results.