8 research outputs found
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a
perceptual-driven approach for single image super resolution that is able to
produce photorealistic images. Despite the visual quality of these generated
images, there is still room for improvement. In this fashion, the model is
extended to further improve the perceptual quality of the images. We have
designed a novel block to replace the one used by the original ESRGAN.
Moreover, we introduce noise inputs to the generator network in order to
exploit stochastic variation. The resulting images present more realistic
textures. The code is available at https://github.com/ncarraz/ESRGANplus .Comment: ICASSP 202
Tarsier: Evolving Noise Injection in Super-Resolution GANs
Super-resolution aims at increasing the resolution and level of detail within
an image. The current state of the art in general single-image super-resolution
is held by NESRGAN+, which injects a Gaussian noise after each residual layer
at training time. In this paper, we harness evolutionary methods to improve
NESRGAN+ by optimizing the noise injection at inference time. More precisely,
we use Diagonal CMA to optimize the injected noise according to a novel
criterion combining quality assessment and realism. Our results are validated
by the PIRM perceptual score and a human study. Our method outperforms NESRGAN+
on several standard super-resolution datasets. More generally, our approach can
be used to optimize any method based on noise injection
Learning Meta-features for AutoML
International audienceThis paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed meta-features with the space of distributions on the hyper-parameter configurations. MetaBu meta-features, learned once and for all, induce a topology on the set of datasets that is exploited to define a distribution of promising hyper-parameter configurations amenable to AutoML. Experiments on the OpenML CC-18 benchmark demonstrate that using MetaBu meta-features boosts the performance of state of the art AutoML systems, (Feurer et al. 2015) and Probabilistic Matrix Factorization (Fusi et al. 2018). Furthermore, the inspection of MetaBu meta-features gives some hints into when an ML algorithm does well. Finally, the topology based on MetaBu meta-features enables to estimate the intrinsic dimensionality of the OpenML benchmark w.r.t. a given ML algorithm or pipeline
Learning Meta-features for AutoML
International audienceThis paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed meta-features with the space of distributions on the hyper-parameter configurations. MetaBu meta-features, learned once and for all, induce a topology on the set of datasets that is exploited to define a distribution of promising hyper-parameter configurations amenable to AutoML. Experiments on the OpenML CC-18 benchmark demonstrate that using MetaBu meta-features boosts the performance of state of the art AutoML systems, (Feurer et al. 2015) and Probabilistic Matrix Factorization (Fusi et al. 2018). Furthermore, the inspection of MetaBu meta-features gives some hints into when an ML algorithm does well. Finally, the topology based on MetaBu meta-features enables to estimate the intrinsic dimensionality of the OpenML benchmark w.r.t. a given ML algorithm or pipeline
Learning Meta-features for AutoML
International audienceThis paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed meta-features with the space of distributions on the hyper-parameter configurations. MetaBu meta-features, learned once and for all, induce a topology on the set of datasets that is exploited to define a distribution of promising hyper-parameter configurations amenable to AutoML. Experiments on the OpenML CC-18 benchmark demonstrate that using MetaBu meta-features boosts the performance of state of the art AutoML systems, (Feurer et al. 2015) and Probabilistic Matrix Factorization (Fusi et al. 2018). Furthermore, the inspection of MetaBu meta-features gives some hints into when an ML algorithm does well. Finally, the topology based on MetaBu meta-features enables to estimate the intrinsic dimensionality of the OpenML benchmark w.r.t. a given ML algorithm or pipeline
Many-Objective Optimization for Diverse Image Generation
In image generation, where diversity is critical, people can express their preferences by choosing among several proposals. Thus, the image generation system can be refined to satisfy the user's needs. In this paper, we focus on multi-objective optimization as a tool for proposing diverse solutions. Multiobjective optimization is the area of research that deals with optimizing several objective functions simultaneously. In particular, it provides numerous solutions corresponding to trade-offs between different objective functions. The goal is to have enough diversity and quality to satisfy the user. However, in computer vision, the choice of objective functions is part of the problem: typically, we have several criteria, and their mixture approximates what we need. We propose a criterion for quantifying the performance in multi-objective optimization based on cross-validation: when optimizing n−1 of the n criteria, the Pareto front should include at least one good solution for the removed n th criterion. After providing evidence for the validity and usefulness of the proposed criterion, we show that the diversity provided by multiobjective optimization is helpful in diverse image generation, namely super-resolution and inspirational generation