22 citations · 26 across the 4 of their papers we have counts for
6 papers
SemiCurv: Semi-Supervised Curvilinear Structure Segmentation
Xun Xu, Manh Cuong Nguyen, Yasin Yazici +3
Recent work on curvilinear structure segmentation has mostly focused on backbone network design and loss engineering. The challenge of collecting labelled data, an expensive and la…
Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime
Xun Xu, Jingyi Liao, Lile Cai +5
Semi-supervised learning (SSL) addresses the lack of labeled data by exploiting large unlabeled data through pseudolabeling. However, in the extremely low-label regime, pseudo labe…
Empirical Analysis of Overfitting and Mode Drop in GAN Training
Yasin Yazici, Chuan-Sheng Foo, Stefan Winkler +2
We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective. We show that when stochasticity is removed from the training procedur…
Classify and Generate: Using Classification Latent Space Representations for Image Generations
Saisubramaniam Gopalakrishnan, Pranshu Ranjan Singh, Yasin Yazici +3
Utilization of classification latent space information for downstream reconstruction and generation is an intriguing and a relatively unexplored area. In general, discriminative re…
Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions
Yasin Yazıcı, Bruno Lecouat, Chuan-Sheng Foo +4
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities. Each data distribution is modeled with a mixture of $…
The Unusual Effectiveness of Averaging in GAN Training
Yasin Yazıcı, Chuan-Sheng Foo, Stefan Winkler +3
We examine two different techniques for parameter averaging in GAN training. Moving Average (MA) computes the time-average of parameters, whereas Exponential Moving Average (EMA) c…