Search Results for author:
Found 7 papers, 3 papers with code
SemiCurv: Semi-Supervised Curvilinear Structure Segmentation
In this work, we propose SemiCurv, a semi-supervised learning (SSL) framework for curvilinear structure segmentation that is able to utilize such unlabelled data to reduce the labelling burden.
Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime
Recent studies combined finetuning (FT) from pretrained weights with SSL to mitigate the challenges and claimed superior results in the low-label regime.
Empirical Analysis of Overfitting and Mode Drop in GAN Training
We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective.
Classify and Generate: Using Classification Latent Space Representations for Image Generations
Utilization of classification latent space information for downstream reconstruction and generation is an intriguing and a relatively unexplored area.
Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities.
The Unusual Effectiveness of Averaging in GAN Training
We examine two different techniques for parameter averaging in GAN training.
Autoregressive Generative Adversarial Networks
Generative Adversarial Networks (GANs) learn a generative model by playing an adversarial game between a generator and an auxiliary discriminator, which classifies data samples vs. generated ones.
