Distilled student models in teacher-student architectures are widely considered for
computational-effective deployment in real-time applications and edge devices. However …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

The current study investigates the robustness of deep learning models for accurate medical
diagnosis systems with a specific focus on their ability to maintain performance in the …

Deep learning has revolutionized computer vision with phenomenal success and
widespread applications. Despite impressive results in complex problems, neural networks …

Deep neural networks have recently been found to be vulnerable to adversarial examples,
which can deceive attacked models with high confidence. This has given rise to significant …

Distributed Multi-exit Neural Networks (MeNNs) use partitioning and early exits to reduce the
cost of neural network inference on low-power sensing systems. Existing MeNNs exhibit …

Transfer learning aims to transfer knowledge or information from a source domain to a
relevant target domain. In this paper, we understand transfer learning from the perspectives …

Machine learning models have become integral to various aspects of daily life, prompting
increased vulnerability to adversarial attacks. Adversarial training is one of the most …

Quantization is a promising technique for reducing the bit-width of deep models to improve
their runtime performance and storage efficiency, and thus becomes a fundamental step for …

Abstract Knowledge transfer is an effective method for learning, particularly useful when
labeled data are limited or when training a model from scratch is too expensive. Most of the …