Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Detecting Unknown Instances Using CNNs
Abstract: Deep convolutional neural networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. However, a vast majority of DCNN-based recognition methods are designed for a closed world, where the primary assumption is that all categories are known a priori. In many real-world applications, this assumption does not necessarily hold. Generally, incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. The goal of a visual recognition system is then to reject samples from unknown classes and classify samples from known classes.
In the first part of my talk, I will present new DCNNs for anomaly detection based on one-class classification. The main idea is to use a zero centered Gaussian noise in the feature space as the pseudo-negative class and train the network using the cross-entropy loss. Also, a method in which both classifier and feature representations are learned together in an end-to-end fashion will be presented. In the second part of the talk, I will present a multi-class category detection using a network which utilizes both global and local information to predict whether the test image belongs to one of the known classes or an unknown category. Specifically, the models is trained using a network to perform image-level category prediction and another network to perform patch-level category prediction. We evaluate the effectiveness all these methods on multiple publicly available datasets and show that these approaches achieve better performance compared to previous state-of-the-art methods.