C2AE: Class Conditioned Auto-Encoder for Open-set Recognition (1904.01198v1)

Abstract: 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.

• The paper introduces a novel two-stage training method that decouples closed-set classification from open-set identification to improve performance on unseen classes.
• It employs class conditioning in the decoder to yield poor reconstructions for mismatched labels, effectively distinguishing unknown samples.
• By integrating Extreme Value Theory to set adaptive thresholds, experiments on multiple image benchmarks demonstrate significant improvements over state-of-the-art methods.

A Study on Class Conditioned Auto-Encoder for Open-set Recognition

Open-set recognition presents a significant challenge in machine learning, particularly in classification tasks where models must identify previously unseen classes during testing. Traditional models often fail outside of their training set, leading to misclassification. In response, the paper "C2AE: Class Conditioned Auto-Encoder for Open-set Recognition" proposes a novel approach utilizing class conditioned auto-encoders (C2AE) to effectively address this problem by separating closed-set classification from open-set identification.

The authors propose a two-stage training process for their C2AE model, each stage targeting a specific sub-task. Initially, the encoder and classifier are trained using conventional closed-set classification techniques. In the second stage, the decoder is trained to reconstruct input samples based on class identity conditioning. This involves creating a match between the input data with its class label and, importantly, producing poor reconstructions when conditioned on an incorrect class label. This process inherently teaches the model to handle known and unknown class samples differently, underlining the decoder's role in distinguishing between them.

A key innovation of the paper lies in employing Extreme Value Theory (EVT) to model reconstruction errors, allowing the model to set a threshold that helps identify whether a test sample belongs to a known or unknown class. This statistical modeling provides a principled way to determine these thresholds, thereby improving the model’s robustness in encountering unseen instances.

Experimentally, the C2AE approach was rigorously tested across several image classification benchmarks, such as MNIST, SVHN, CIFAR10, CIFAR-Plus, and TinyImageNet datasets. The authors report significant performance improvements over existing state-of-the-art open-set recognition methods like SoftMax, OpenMax, and others that rely on augmented data or modified output layer activations. Notably, C2AE demonstrated superior open-set identification capabilities, especially in datasets characterized by object diversity and complexity, highlighting its adaptability and precision.

The implications of this approach are manifold. By dividing the open-set recognition problem and optimizing its sub-tasks separately, better foundational models for tasks requiring adaptable and extensible recognition capabilities are realized. The authors suggest that this method’s applicability could extend beyond image classification, potentially enhancing any domain where models must discern familiar from unfamiliar inputs dynamically, paving the way for advancements in anomaly detection, security systems, and real-time data analysis.

Future exploration may involve integrating generative models like GANs or VAEs with the C2AE framework to further refine the unknown sample synthesis during training and enhance the generalization of the model. This could amplify the robustness of open-set classifiers in even more expansive domains, potentially revolutionizing approaches to unknown object classification in artificial intelligence.