7,600 research outputs found
Neural Degradation Representation Learning for All-In-One Image Restoration
Existing methods have demonstrated effective performance on a single
degradation type. In practical applications, however, the degradation is often
unknown, and the mismatch between the model and the degradation will result in
a severe performance drop. In this paper, we propose an all-in-one image
restoration network that tackles multiple degradations. Due to the
heterogeneous nature of different types of degradations, it is difficult to
process multiple degradations in a single network. To this end, we propose to
learn a neural degradation representation (NDR) that captures the underlying
characteristics of various degradations. The learned NDR decomposes different
types of degradations adaptively, similar to a neural dictionary that
represents basic degradation components. Subsequently, we develop a degradation
query module and a degradation injection module to effectively recognize and
utilize the specific degradation based on NDR, enabling the all-in-one
restoration ability for multiple degradations. Moreover, we propose a
bidirectional optimization strategy to effectively drive NDR to learn the
degradation representation by optimizing the degradation and restoration
processes alternately. Comprehensive experiments on representative types of
degradations (including noise, haze, rain, and downsampling) demonstrate the
effectiveness and generalization capability of our method
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
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