To address the challenges of long-tailed classification, researchers have proposed several
approaches to reduce model bias, most of which assume that classes with few samples are …

Neural network-based approaches can achieve high accuracy in various medical image
segmentation tasks. However, they generally require large labelled datasets for supervised …

Sampling from diffusion probabilistic models (DPMs) can be viewed as a piecewise
distribution transformation, which generally requires hundreds or thousands of steps of the …

Generative adversarial networks (GANs) have attracted huge attention due to its capability to
generate visual realistic images. However, most of the existing models suffer from the mode …

In scenarios with long-tailed distributions, the model's ability to identify tail classes is limited
due to the under-representation of tail samples. Class rebalancing, information …

We introduce a neural network inspired by Google's Inception model to compute the Hodge
number h 1, 1 of complete intersection Calabi–Yau (CICY) 3-folds. This architecture …

Abstract Black-Box Knowledge Distillation (B2KD) is a formulated problem for cloud-to-edge
model compression with invisible data and models hosted on the server. B2KD faces …

Detecting targets in dynamically changing clutter environments has always been
challenging for radar techniques. The innovative matrix information geometry-based (MIG) …

Graph neural networks (GNNs) have demonstrated impressive achievements in diverse
graph tasks, and research on their expressive power has experienced significant growth in …

Existing learning-based methods usually train a point cloud upsampling model with
synthesized, paired sparse-dense point clouds. However, the distribution gap between …