Robust Classification with Convolutional Prototype Learning (1805.03438v1)

Abstract: Convolutional neural networks (CNNs) have been widely used for image classification. Despite its high accuracies, CNN has been shown to be easily fooled by some adversarial examples, indicating that CNN is not robust enough for pattern classification. In this paper, we argue that the lack of robustness for CNN is caused by the softmax layer, which is a totally discriminative model and based on the assumption of closed world (i.e., with a fixed number of categories). To improve the robustness, we propose a novel learning framework called convolutional prototype learning (CPL). The advantage of using prototypes is that it can well handle the open world recognition problem and therefore improve the robustness. Under the framework of CPL, we design multiple classification criteria to train the network. Moreover, a prototype loss (PL) is proposed as a regularization to improve the intra-class compactness of the feature representation, which can be viewed as a generative model based on the Gaussian assumption of different classes. Experiments on several datasets demonstrate that CPL can achieve comparable or even better results than traditional CNN, and from the robustness perspective, CPL shows great advantages for both the rejection and incremental category learning tasks.

• The paper presents Convolutional Prototype Learning (CPL) as a robust alternative to softmax, improving classification for adversarial and open-world challenges.
• CPL employs prototype-based classification with Euclidean distance and a novel prototype loss to enforce intra-class compactness and facilitate incremental learning.
• Experimental results on MNIST, CIFAR-10, and OLHWDB demonstrate CPL's competitive performance in handling unseen classes and reducing false categorization.

Robust Classification with Convolutional Prototype Learning

The paper "Robust Classification with Convolutional Prototype Learning" by Hong-Ming Yang et al. presents a novel approach to improving the robustness of Convolutional Neural Networks (CNNs) in pattern classification tasks, specifically targeting challenges related to adversarial examples and open world recognition.

CNNs, acclaimed for their high accuracy in computer vision tasks, are often critiqued for their vulnerability to adversarial attacks and their inadequacy in handling samples from unseen classes. The authors argue that the shortcomings primarily emerge from the softmax layer, which presupposes a closed world with a fixed number of categories and functions as a purely discriminative model. To address these issues, the paper proposes Convolutional Prototype Learning (CPL), which replaces the softmax layer with a framework incorporating multiple prototypes per class within the feature space.

Framework and Methodology

CPL's architecture consists of convolutional layers for feature extraction followed by prototype-based classification. Instead of partitions formed by softmax, CPL assigns prototypes representing class patterns and determines class membership by finding the closest prototype using Euclidean distance. The training involves multiple classification criteria integrating a novel prototype loss (PL), promoting intra-class compactness by assuming a Gaussian distribution for each class. This integration not only enhances discrimination power but also facilitates rejection and incremental learning, maintaining model robustness.

Experimental Verification

CPL was assessed on datasets such as MNIST, CIFAR-10, and OLHWDB. The experiments reveal competitive or improved performance compared to traditional CNNs, particularly in rejection and open-set scenarios. For instance, while using the MNIST dataset, CPL achieved notable improvements in classifying unseen classes with minimal false categorization. The paper further confirms CPL's efficacy in class-incremental learning without retraining necessity, thus allowing scalability with additional classes.

Implications and Future Directions

This approach holds significant implications for implementing robust AI systems that cope with dynamically changing environments and sparse data scenarios. CPL marries the generative modeling of features with discriminative classification, paving the way for enhanced handling of open world problems and adversarial resilience. Further exploration could involve adapting CPL for varying sample sizes and extending its application in other domains with complex class relationships. The foundational aspects of such hybrid discriminative-generative frameworks may inspire future advancements in robust AI models capable of self-adaptation and incremental learning.

In conclusion, CPL demonstrates a promising direction in fortifying CNNs against real-world complexities, suggesting pathways for seamless integration into diverse AI applications. As AI systems increasingly interact with unstructured and evolving data, frameworks like CPL that focus on robustness and flexibility could become an integral part of next-generation AI solutions.