In this work, we explore various methods to evaluate a deep learning network trained in a supervised fashion, specifically in a figure-ground (FF) setting. We examine different evaluation strategies and compare their impact on the final accuracy. Our findings reveal that using a neutral label to generate features for all classes can lead to instability and lower accuracy compared to other methods. To overcome this challenge, we experimented with various techniques but found no success. Instead, we recommend using the technique proposed by (Hinton, 2022), which involves freezing the whole network except for the auxiliary head, using normal negative labels instead of neutral ones, and using a classifier consisting of multiple layers. By employing these modifications, we were able to achieve better results and improve the accuracy of our network.
Section 1: Introduction
In this section, we provide an overview of the context and introduce the main topic of discussion: evaluating deep learning networks in FF settings. We explain that there are several methods to evaluate a trained network, but some of these methods may not be effective or lead to instability.
Section 2: Evaluation Strategies
In this section, we explore various evaluation strategies and compare their impact on the final accuracy. We discuss the different techniques used in FF settings and how they affect the performance of the network. We also explain that using a neutral label to generate features for all classes can lead to instability and lower accuracy.
Section 3: Challenges and Modifications
In this section, we discuss the challenges faced while evaluating deep learning networks in FF settings and propose modifications to overcome these challenges. We explain that some techniques, such as freezing the whole network except for the auxiliary head, using normal negative labels instead of neutral ones, and using a classifier consisting of multiple layers, can improve the accuracy of the network.
Conclusion
In conclusion, evaluating deep learning networks in FF settings is crucial to ensure their performance and accuracy. While there are various methods to evaluate these networks, some of them may not be effective or lead to instability. By employing modifications such as those proposed by (Hinton, 2022), we can improve the accuracy of the network and overcome the challenges faced during evaluation.
