no code implementations • 11 Oct 2022 • Jonghwa Yim, Minjae Kim
To alleviate the process of generating lots of samples repetitively, in this article, we propose to take a desired output, a style image, as an additional condition without re-training the transformer.
no code implementations • 13 May 2022 • GunHee Lee, Jonghwa Yim, Chanran Kim, Minjae Kim
Despite recent success in conditional image synthesis, prevalent input conditions such as semantics and edges are not clear enough to express `Linear (Ridges)' and `Planar (Scale)' representations.
no code implementations • 5 Aug 2020 • Jonghwa Yim, Sang Hwan Kim
While MTL benefits from the positive transfer of information from multiple tasks, in a real environment, tasks inevitably have a conflict between them during the learning phase, called negative transfer.
no code implementations • ECCV 2020 • Jonghwa Yim, Jisung Yoo, Won-joon Do, Beomsu Kim, Jihwan Choe
Unlike conventional style transfer, new technique FST can extract and transfer custom filter style from a filtered style image to a content image.
no code implementations • 27 Nov 2018 • Jonghwa Yim, Junghun James Kim, Daekyu Shin
However, since deep neural network emerged, the performance of visual search becomes high enough to apply it in many industries from 3D data to multimodal data.
no code implementations • 23 Oct 2017 • Jonghwa Yim, Kyung-Ah Sohn
Therefore, in this study, we exhaustively research skip connections of state-of-the-art deep convolutional networks and investigate the characteristics of the features from each intermediate layer.
no code implementations • 18 Oct 2017 • Jonghwa Yim, Kyung-Ah Sohn
Despite the appeal of deep neural networks that largely replace the traditional handmade filters, they still suffer from isolated cases that cannot be properly handled only by the training of convolutional filters.