4 accepted papers
Despite their success in specific scenarios, existing foundation agents still struggle to generalize across various virtual scenarios, mainly due to the dramatically different encapsulations of environments with manually designed observation and action spaces. To handle this issue, we propose the Ge…
Generative data augmentation (GDA) leverages generative models to enrich training sets with entirely new samples drawn from the modeled data distribution to achieve performance gains. However, the usage of the mighty contemporary diffusion models in GDA remains impractical: *i)* their thousand-step…
Optical imaging capable of resolving nanoscale features would revolutionize scientific research and engineering applications across biomedicine, smart manufacturing, and semiconductor quality control. However, due to the physical phenomenon of diffraction, the optical resolution is limited to approx…
Learning representations to capture the very fundamental understanding of the world is a key challenge in machine learning. The hierarchical structure of explanatory factors hidden in data is such a general representation and could be potentially achieved with a hierarchical VAE. However training a…