5 papers
Toward Architecture-Agnostic Local Control of Posterior Collapse in VAEs
Hyunsoo Song, Seungwhan Kim, Seungkyu Lee
Variational autoencoders (VAEs), one of the most widely used generative models, are known to suffer from posterior collapse, a phenomenon that reduces the diversity of generated sa…
Adversarial Wear and Tear: Exploiting Natural Damage for Generating Physical-World Adversarial Examples
Samra Irshad, Seungkyu Lee, Nassir Navab +2
The presence of adversarial examples in the physical world poses significant challenges to the deployment of Deep Neural Networks in safety-critical applications such as autonomous…
Beta-Sigma VAE: Separating beta and decoder variance in Gaussian variational autoencoder
Seunghwan Kim, Seungkyu Lee
Variational autoencoder (VAE) is an established generative model but is notorious for its blurriness. In this work, we investigate the blurry output problem of VAE and resolve it,…
Neural Radiance Fields for Transparent Object Using Visual Hull
Heechan Yoon, Seungkyu Lee
Unlike opaque object, novel view synthesis of transparent object is a challenging task, because transparent object refracts light of background causing visual distortions on the tr…
Single Image Reflection Removal with Patch Reflectance Prior
Dongshen Han, Heechan Yoon, Hyukmin Kwon +4
Single Image Reflection Removal (SIRR) in real-world images is a challenging task due to diverse image degradations occurring on the glass surface during light transmission and ref…