Geometric Transformation-Based Network Ensemble for Open-Set Recognition

Pramuditha Perera, Vishal M. Patel

Abstract: Open-set recognition focuses on the problem of determining whether a given query image belongs to one of the classes known to the network. Recent works in open-set recognition have attempted to solve this problem using an external deep network by either modeling known class samples or simulating open-set samples at the expense of more parameters. In this work, we propose a modified network structure and an inference rule that leads to better open-set recognition performance. First, we show that networks learn different representations when they are trained on datasets subjected to extreme geometric transformations. By exploiting this fact, the proposed mechanism learns multiple representations from the same set of image data using a set of parallel networks with identical structures. During inference, decisions of all independent networks are fused using majority voting to arrive at predictions. The proposed method obtains state-of-the-art open-set detection performance on multiple object recognition datasets.