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Found 8 papers, 7 papers with code
Evaluating tree-based imputation methods as an alternative to MICE PMM for drawing inference in empirical studies
However, the performance and validity are not completely understood, particularly compared to the standard MICE PMM.
Generalized Real-World Super-Resolution through Adversarial Robustness
Instead of learning a dataset-specific degradation, we employ adversarial attacks to create difficult examples that target the model's weaknesses.
Zero-Pair Image to Image Translation using Domain Conditional Normalization
In this paper, we propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation, i. e., translating between two domains which have no paired training data available but each have paired training data with a third domain.
SMILE: Semantically-guided Multi-attribute Image and Layout Editing
Additionally, our method is capable of adding, removing or changing either fine-grained or coarse attributes by using an image as a reference or by exploring the style distribution space, and it can be easily extended to head-swapping and face-reenactment applications without being trained on videos.
AIM 2020 Challenge on Image Extreme Inpainting
This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting.
SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects
In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former.
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution
To the best of our knowledge, DeepSEE is the first method to leverage semantic maps for explorative super-resolution.
SMIT: Stochastic Multi-Label Image-to-Image Translation
This problem is highly challenging due to three main reasons: (i) unpaired datasets, (ii) multiple attributes, and (iii) the multimodality (e. g., style) associated with the translation.
