Selective Classification for Deep Neural Networks (1705.08500v2)

Abstract: Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs). These techniques can potentially significantly improve DNNs prediction performance by trading-off coverage. In this paper we propose a method to construct a selective classifier given a trained neural network. Our method allows a user to set a desired risk level. At test time, the classifier rejects instances as needed, to grant the desired risk (with high probability). Empirical results over CIFAR and ImageNet convincingly demonstrate the viability of our method, which opens up possibilities to operate DNNs in mission-critical applications. For example, using our method an unprecedented 2% error in top-5 ImageNet classification can be guaranteed with probability 99.9%, and almost 60% test coverage.

• The paper presents a novel selective classification method that sets a risk threshold to control uncertainty in deep neural network predictions.
• It uses softmax response and Monte Carlo dropout techniques, validated on datasets like CIFAR-10, CIFAR-100, and ImageNet with VGG-16 and RESNET-50.
• The approach guarantees low top-5 error rates and up to 60% coverage, enhancing the safety and reliability of DNNs in critical applications.

Selective Classification for Deep Neural Networks

Selective classification, a concept rooted in the ability of systems to recognize and abstain from uncertain predictions, has yet to be widely explored within the domain of deep neural networks (DNNs). The paper "Selective Classification for Deep Neural Networks" by Yonatan Geifman and Ran El-Yaniv introduces a novel method to incorporate selective classification into existing DNNs, allowing for substantial improvement in prediction accuracy by strategically controlling uncertainty through rejection of certain cases.

Methodology

The proposed methodology centers on constructing a selective classifier using a pre-trained neural network. This classifier, designed to meet a user-defined risk threshold, can reject test instances that exceed an acceptable risk level. Two primary mechanisms are employed: the softmax response (SR) and Monte-Carlo dropout (MC-dropout). SR relies on the maximum response of the softmax layer, while MC-dropout measures uncertainty by sampling stochastic outputs through dropout.

The authors implemented a learning algorithm named Selection with Guaranteed Risk (SGR), which constructs a selective classifier (f, g) with a classifier f and a rejection function g. This selective classifier optimizes for a specified risk level, ensuring, with high probability, that the actual test error does not exceed this level. Their empirical analysis focused on datasets including CIFAR-10, CIFAR-100, and ImageNet, using architectures such as VGG-16 and RESNET-50.

Empirical Results

The empirical evaluations reveal remarkable efficacy of SR and MC-dropout methods, particularly with SR demonstrating superior performance on ImageNet. Using VGG-16 and RESNET-50 architectures, the authors report results such as a guaranteed 2% top-5 error rate on ImageNet with 99.9% confidence and nearly 60% coverage. These findings suggest that DNNs can indeed operate in high-stakes applications, such as autonomous driving and medical diagnostics, with assured performance metrics.

Implications and Future Directions

The introduction of selective classification into DNNs implies a significant step towards enhancing reliability and safety in critical decision-making systems. The practical applications are diverse—ranging from autonomous systems that disengage in uncertain scenarios to financial models where risk is tightly controlled.

Theoretical implications include the potential for improving ensemble methods, which have traditionally faced scalability issues within the deep learning context. The methodology further aligns with active learning paradigms by prioritizing cases where additional human input may be valuable.

Future developments may explore simultaneous training of classification and selection functions, refining the ability to jointly optimize error rates and coverage. Moreover, extending selective classification techniques to regression tasks and other loss functions represents a fertile research territory.

In conclusion, this paper offers a comprehensive approach to integrate selective classification into DNNs, establishing a pathway for more robust application across various fields. The proposed methods, if further developed and refined, promise significant contributions to the advancement of DNN reliability and efficacy.