Enhancing adaptivity of active sonar classifier considering loss landscape under dataset shift

Abstract

We study the loss landscape of deep neural networks and use it to solve dataset shift problems in active sonar classification. The dataset shift degrades the generalization performance of supervised learning-based classifiers. When test data samples are available, fine-tuning methods can be used to mitigate the performance decrease. However, they can induce catastrophic forgetting and negative transfer because fine-tuned weights could be overfitted to the test data distribution. These problems are more severe in active sonar datasets with small amounts and less diversity. To mitigate the two problems in conventional fine-tuning, we explore the loss landscape of supervised learning-based active sonar classifiers and apply it to derive adaptative weights from pre-trained weights. Our results showed promising performance.