C2AE: Class Conditioned Auto-Encoder for Open-set Recognition

Models trained for classification often assume that all testing classes are known while training. As a result, when presented with an unknown class during testing, such closed-set assumption forces the model to classify it as one of the known classes. However, in a real world scenario, classification models are likely to encounter such examples. Hence, identifying those examples as unknown becomes critical to model performance. A potential solution to overcome this problem lies in a class of learning problems known as open-set recognition. It refers to the problem of identifying the unknown classes during testing, while maintaining performance on the known classes. In this paper, we propose an open-set recognition algorithm using class conditioned auto-encoders with novel training and testing methodology. In contrast to previous methods, training procedure is divided in two sub-tasks, 1. closed-set classification and, 2. open-set identification (i.e. identifying a class as known or unknown). Encoder learns the first task following the closed-set classification training pipeline, whereas decoder learns the second task by reconstructing conditioned on class identity. Furthermore, we model reconstruction errors using the Extreme Value Theory of statistical modeling to find the threshold for identifying known/unknown class samples. Experiments performed on multiple image classification datasets show proposed method performs significantly better than state of the art.Comment: CVPR2019 (Oral

Learning with Unavailable Data: Generalized and Open Zero-Shot Learning

The field of visual object recognition has seen a significant progress in recent years thanks to the availability of large-scale annotated datasets. However, labelling a large amount of data is difficult and costly and can be simply infeasible for some classes due to the long-tail instances distribution problem. Zero-Shot Learning (ZSL) is a framework that consider the case in which for some of the classes no labeled training examples are available to train the model. To solve the problem a multi-modal source of information, the class (semantic) embeddings, is exploited to extract knowledge from the available classes, the seen classes, and recognize novel categories for which the class embeddings is the only information available, namely, the unseen classes. To directly targeting the extreme imbalance in the data, in this thesis, we first propose a methodology to improve synthetic data generation for the unseen classes through their class embeddings. Second, we propose to generalize the Zero-Shot Learning framework towards a more competitive and real-world oriented scenario. Thus, we formalize the problem of Open Zero-Shot Learning as the problem of recognizing seen and unseen classes, as in ZSL, while also rejecting instances from unknown categories, for which neither visual data nor class embeddings are provided. Finally, we propose methodologies to not only generate unseen categories, but also the unknown ones

MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration

Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability to identify instances of novel classes while maintaining discriminative capability for closed-set classification. OpenMax was the first deep neural network-based approach to address open-set recognition by calibrating the predictive scores of a standard closed-set classification network. In this paper we present MetaMax, a more effective post-processing technique that improves upon contemporary methods by directly modeling class activation vectors. MetaMax removes the need for computing class mean activation vectors (MAVs) and distances between a query image and a class MAV as required in OpenMax. Experimental results show that MetaMax outperforms OpenMax and is comparable in performance to other state-of-the-art approaches.Comment: To be presented at the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshop on Dealing with Novelty in Open Worlds (DNOW