Bowen Tian

Bowen Tian, Wenshuo Chen, Zexi Li et al.

How far are we really from automatically generating neural networks? While neural network weight generation shows promise, current approaches struggle with generalization to unseen tasks and practical application exploration. To address this, we propose T2W, a diffusion transformer framework that generates task-specific weights conditioned on natural language descriptions. T2W hierarchically processes network parameters into uniform blocks, integrates text embeddings from CLIP via a prior attention mechanism, and employs adversarial training with weight-space augmentation to enhance generalization. Experiments on Cifar100, Caltech256, and TinyImageNet demonstrate T2W's ability to produce high-quality weights for unseen tasks, outperforming optimization-based initialization and enabling novel applications such as weight enhancement and text-guided model fusion. Our work bridges textual semantics with weight-space dynamics, supported by an open-source dataset of text-weight pairs, advancing the practicality of generative models in neural network parameter synthesis. Our code is available on Github.

Bowen Tian, Zhengyang Xu, Mingqiang Wu et al.

With the pervasive integration of computer applications across industries, the presence of vulnerabilities within code bases poses significant risks. The diversity of software ecosystems coupled with the intricate nature of modern software engineering has led to a shift from manual code vulnerability identification towards the adoption of automated tools. Among these, deep learning-based approaches have risen to prominence due to their superior accuracy; however, these methodologies encounter several obstacles. Primarily, they necessitate extensive labeled datasets and prolonged training periods, and given the rapid emergence of new vulnerabilities, the frequent retraining of models becomes a resource-intensive endeavor, thereby limiting their applicability in cutting-edge scenarios. To mitigate these challenges, this paper introduces the \underline{\textbf{YOTO}}--\underline{\textbf{Y}}ou \underline{\textbf{O}}nly \underline{\textbf{T}}rain \underline{\textbf{O}}nce framework. This innovative approach facilitates the integration of multiple types of vulnerability detection models via parameter fusion, eliminating the need for joint training. Consequently, YOTO enables swift adaptation to newly discovered vulnerabilities, significantly reducing both the time and computational resources required for model updates.