4 accepted papers
Text-to-audio generation models (TAG) have achieved significant advances in generating audio conditioned on text descriptions. However, a critical challenge lies in the lack of transparency regarding how each textual input impacts the generated audio. To address this issue, we introduce AudioGenX, a…
Diffusion models generate high-quality images through progressive denoising but are computationally intensive due to large model sizes and repeated sampling. Knowledge distillation--transferring knowledge from a complex teacher to a simpler student model--has been widely studied in recognition tasks…
Diffusion models have emerged as a cornerstone of generative modeling, capable of producing high-quality images through a progressive denoising process. However, their remarkable performance comes with substantial computational costs, driven by large model sizes and the need for multiple sampling st…
Node representation learning, such as Graph Neural Networks (GNNs), has become one of the important learning methods in machine learning, and the demand for reliable explanation generation is growing. Despite extensive research on explanation generation for supervised node representation learning, e…