Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation

A decentralized data marketplace named D2M has been introduced, aiming to enhance collaborative machine learning by integrating federated learning, blockchain arbitration, and economic incentives into a single framework. This approach addresses the limitations of existing methods, such as the reliance on trusted aggregators in federated learning and the computational challenges faced by blockchain systems.

The empirical evaluation of Frank-Wolfe methods for constructing white-box adversarial attacks highlights the need for efficient adversarial attack construction in neural networks, particularly focusing on numerical optimization techniques. The study emphasizes the application of modified Frank-Wolfe methods to enhance the robustness of neural networks against adversarial threats, utilizing datasets like MNIST and CIFAR-10 for testing.

AEBNAS introduces a hardware-aware Neural Architecture Search (NAS) framework designed to enhance early-exit networks, which optimize energy consumption and latency in deep learning models by allowing for intermediate exit branches based on input complexity. This approach aims to balance efficiency and performance, particularly for resource-constrained devices.

The recent introduction of VLM-NCD, a novel class discovery framework utilizing vision-based large language models, aims to enhance the classification and discovery of unknown classes from unlabelled data. This approach addresses the limitations of existing methods that primarily rely on visual features, which often struggle with feature discriminability and data distribution challenges.