Pre-trained models have become the preferred backbone due to the expansion of model
parameters, with techniques like Parameter-Efficient Fine-Tuning (PEFTs) typically fixing the …

The expansion of model parameters underscores the significance of pre-trained models;
however, the constraints encountered during model deployment necessitate models of …

In practice, we usually need to build variable-sized models adapting for diverse resource
constraints in different application scenarios, where weight initialization is an important step …

Diffusion models often face slow convergence, and existing efficient training techniques,
such as Parameter-Efficient Fine-Tuning (PEFT), are primarily designed for fine-tuning pre …

As the size of deep neural network (DNN) models and datasets increases, distributed
training becomes popular to reduce the training time. However, a severe communication …

Cross-Domain Few-Shot Learning (CD-FSL) aims to transfer knowledge from seen source
domains to unseen target domains, which is crucial for evaluating the generalization and …

In practical scenarios, it is necessary to build variable-sized models to accommodate diverse
resource constraints, where weight initialization serves as a crucial step preceding training …