Convolutional neural networks (CNNs) have achieved impressive results on imbalanced
image data, but they still have difficulty generalizing to minority classes and their decisions …

Recent studies propose membership inference (MI) attacks on deep models, where the goal
is to infer if a sample has been used in the training process. Despite their apparent success …

Machine learning (ML) systems have achieved remarkable performance across a wide area
of applications. However, they frequently exhibit unfair behaviors in sensitive application …

Existing paradigms of pushing the state of the art require exponentially more training data in
many fields. Coreset selection seeks to mitigate this growing demand by identifying the most …

Knowledge Distillation transfers knowledge learned by a teacher network to a student
network. A common mode of knowledge transfer is directly using the teacher network's …

This paper introduces a computationally efficient Query-by-Committee (QBC) algorithm
specifically designed for deep active learning. This approach leverages the concept of …

With the successful applications of DNNs in many real-world tasks, model's robustness has
raised public concern. Recently the robustness of deep models is often evaluated by …

This article discovers that the neural network (NN) with lower decision boundary (DB)
variability has better generalizability. Two new notions, algorithm DB variability and-data DB …

Currently, interpretability methods focus more on less objective human-understandable
semantics. To objectify and standardize interpretability research, in this study, we provide …

Active learning is a machine learning paradigm that aims to improve the performance of a
model by strategically selecting and querying unlabeled data. One effective selection …