Dataset condensation aims at reducing the network training effort through condensing a
cumbersome training set into a compact synthetic one. State-of-the-art approaches largely …

Label noise has been a practical challenge in deep learning due to the strong capability of
deep neural networks in fitting all training data. Prior literature primarily resorts to sample …

Numerous Deep Learning (DL) classification models have been developed for a large
spectrum of medical image analysis applications, which promises to reshape various facets …

Abstract Numerous Deep Learning (DL) classification models have been developed for a
large spectrum of medical image analysis applications, which promises to reshape various …

The recyclability of domestic waste plays a crucial role in the modern society, which helps
reduce multiple types of pollution and brings economic effect. To achieve this goal, garbage …

Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised
models due to its effect in hurting the generalization performance of deep neural networks …

There has been significant attention devoted to the effectiveness of various domains, such
as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the …

Deep neural networks have empowered accurate device-free human activity recognition,
which has wide applications. Deep models can extract robust features from various sensors …

Face recognition has achieved significant progress in deep learning era due to the ultra-
large-scale and welllabeled datasets. However, training on the outsize datasets is time …

Performances of deep neural networks are prone to be degraded by label noise due to their
powerful capability in fitting training data. Deeming low-loss instances as clean data is one …