6 accepted papers
Data-Efficient Generative Adversarial Nets (DE-GANs) have become more and more popular in recent years. Existing methods apply data augmentation, noise injection and pre-trained models to maximumly increase the number of training samples thus improving the training of DE-GANs. However, none of these…
Large Language Models (LLMs), exemplified by Claude and LLama, have exhibited impressive proficiency in tackling a myriad of Natural Language Processing (NLP) tasks. Yet, in pursuit of the ambitious goal of attaining Artificial General Intelligence (AGI), there remains ample room for enhancing LLM c…
Diffusion-based generative models (diffusion models) often require a large amount of data to train a score-based model that learns the score function of the data distribution through denoising score matching. However, collecting and cleaning such data can be expensive, time-consuming, and even infea…
The frame quality deterioration problem is one of the main challenges in the field of video understanding. To compensate for the information loss caused by deteriorated frames, recent approaches exploit transformer-based integration modules to obtain spatio-temporal information. However, these integ…
Generative adversarial network (GAN) is a powerful generative model. However, it suffers from two key problems which are convergence instability and mode collapse. Recently, progressive growing of GANs for improving quality, stability and variation (PGGAN) is proposed to better solve these two probl…
Snake-like manipulators can navigate and perform manipulation in confined spaces. Their recent implementations in surgical robots attracted a lot of attentions. These slender manipulators usually possess either a hyper-redundant articulated vertebrate structure or a continuum one. Primary design con…