Effects of Common Regularization Techniques on Open-Set Recognition
This addresses the problem of enabling classification models to identify unknown inputs for real-world applications, but it is incremental as it examines existing regularization methods.
The paper investigated how common regularization techniques affect Open-Set Recognition performance, showing empirically that these methods can significantly improve it and providing insights into the relationship between accuracy and open-set performance.
In recent years there has been increasing interest in the field of Open-Set Recognition, which allows a classification model to identify inputs as "unknown" when it encounters an object or class not in the training set. This ability to flag unknown inputs is of vital importance to many real world classification applications. As almost all modern training methods for neural networks use extensive amounts of regularization for generalization, it is therefore important to examine how regularization techniques impact the ability of a model to perform Open-Set Recognition. In this work, we examine the relationship between common regularization techniques and Open-Set Recognition performance. Our experiments are agnostic to the specific open-set detection algorithm and examine the effects across a wide range of datasets. We show empirically that regularization methods can provide significant improvements to Open-Set Recognition performance, and we provide new insights into the relationship between accuracy and Open-Set performance.