Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Foundation models (FM) have demonstrated remarkable performance across a wide range
of tasks (especially in the fields of natural language processing and computer vision) …

Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Abstract Unsupervised Domain Adaptation (UDA) can tackle the challenge that
convolutional neural network (CNN)-based approaches for semantic segmentation heavily …

Federated Distillation (FD) has recently attracted increasing attention for its efficiency in
aggregating multiple diverse local models trained from statistically heterogeneous data of …

Despite the tremendous progress on data-free knowledge distillation (DFKD) based on
synthetic data generation, there are still limitations in diverse and efficient data synthesis. It …

Neural network quantization is an effective way to compress deep models and improve their
execution latency and energy efficiency, so that they can be deployed on mobile or …

Network quantization is an essential procedure in deep learning for development of efficient
fixed-point inference models on mobile or edge platforms. However, as datasets grow larger …

It has been recently demonstrated that multi-generational self-distillation can improve
generalization. Despite this intriguing observation, reasons for the enhancement remain …