709 research outputs found
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification
With the popularity of deep neural networks (DNNs), model interpretability is
becoming a critical concern. Many approaches have been developed to tackle the
problem through post-hoc analysis, such as explaining how predictions are made
or understanding the meaning of neurons in middle layers. Nevertheless, these
methods can only discover the patterns or rules that naturally exist in models.
In this work, rather than relying on post-hoc schemes, we proactively instill
knowledge to alter the representation of human-understandable concepts in
hidden layers. Specifically, we use a hierarchical tree of semantic concepts to
store the knowledge, which is leveraged to regularize the representations of
image data instances while training deep models. The axes of the latent space
are aligned with the semantic concepts, where the hierarchical relations
between concepts are also preserved. Experiments on real-world image datasets
show that our method improves model interpretability, showing better
disentanglement of semantic concepts, without negatively affecting model
classification performance
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
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