4 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
Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili
We consider hate speech detection through keyword spotting on radio
broadcasts. One approach is to build an automatic speech recognition (ASR)
system for the target low-resource language. We compare this to using acoustic
word embedding (AWE) models that map speech segments to a space where matching
words have similar vectors. We specifically use a multilingual AWE model
trained on labelled data from well-resourced languages to spot keywords in data
in the unseen target language. In contrast to ASR, the AWE approach only
requires a few keyword exemplars. In controlled experiments on Wolof and
Swahili where training and test data are from the same domain, an ASR model
trained on just five minutes of data outperforms the AWE approach. But in an
in-the-wild test on Swahili radio broadcasts with actual hate speech keywords,
the AWE model (using one minute of template data) is more robust, giving
similar performance to an ASR system trained on 30 hours of labelled data.Comment: Accepted to Interspeech 202
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