This paper investigates a challenging problem of zero-shot learning in t...
Federated learning (FL) enables collaborative model training among
distr...
This paper provides a novel framework for single-domain generalized obje...
Diffusion-based Generative Models (DGMs) have achieved unparalleled
perf...
Model substructure learning aims to find an invariant network substructu...
Malware open-set recognition (MOSR) aims at jointly classifying malware
...
Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts...
We study the challenging task of malware recognition on both known and n...
This paper investigates a new, practical, but challenging problem named
...
Mixed-precision quantization mostly predetermines the model bit-width
se...
Recent studies show that even highly biased dense networks contain an
un...
Network pruning is a promising way to generate light but accurate models...
Compositional Zero-Shot Learning (CZSL) aims to recognize novel concepts...
Recent years have witnessed the dramatic growth of Internet video traffi...
Federated learning (FL) has emerged as a promising privacy-preserving
di...
Multi-label zero-shot learning extends conventional single-label zero-sh...
We study the recent emerging personalized federated learning (PFL) that ...
Zero-shot learning (ZSL) aims at recognizing unseen class examples (e.g....
Zero-shot learning aims at recognizing unseen classes (no training examp...
Zero-shot learning (ZSL) aims at recognizing unseen classes with knowled...
In most recent years, deep convolutional neural networks (DCNNs) based i...
Forecasting the future traffic flow distribution in an area is an import...
Unsupervised learning is becoming more and more important recently. As o...
In most recent years, zero-shot recognition (ZSR) has gained increasing
...