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The problem of dealing with missing or incomplete data in machine
learning and computer vision arises in many applications. Recent
strategies make use of generative models to impute missing or
corrupted data. Advances in computer vision using deep generative
models have found applications in image/video processing, such as
denoising, restoration, super-resolution, or inpainting. Inpainting
and Denoising Challenges comprises recent efforts dealing with
image and video inpainting tasks. This includes winning solutions
to the ChaLearn Looking at People inpainting and denoising
challenges: human pose recovery, video de-captioning and
fingerprint restoration. This volume starts with a wide review on
image denoising, retracing and comparing various methods from the
pioneer signal processing methods, to machine learning approaches
with sparse and low-rank models, and recent deep learning
architectures with autoencoders and variants. The following
chapters present results from the Challenge, including three
competition tasks at WCCI and ECML 2018. The top best approaches
submitted by participants are described, showing interesting
contributions and innovating methods. The last two chapters propose
novel contributions and highlight new applications that benefit
from image/video inpainting.
The problem of dealing with missing or incomplete data in machine
learning and computer vision arises in many applications. Recent
strategies make use of generative models to impute missing or
corrupted data. Advances in computer vision using deep generative
models have found applications in image/video processing, such as
denoising, restoration, super-resolution, or inpainting. Inpainting
and Denoising Challenges comprises recent efforts dealing with
image and video inpainting tasks. This includes winning solutions
to the ChaLearn Looking at People inpainting and denoising
challenges: human pose recovery, video de-captioning and
fingerprint restoration. This volume starts with a wide review on
image denoising, retracing and comparing various methods from the
pioneer signal processing methods, to machine learning approaches
with sparse and low-rank models, and recent deep learning
architectures with autoencoders and variants. The following
chapters present results from the Challenge, including three
competition tasks at WCCI and ECML 2018. The top best approaches
submitted by participants are described, showing interesting
contributions and innovating methods. The last two chapters propose
novel contributions and highlight new applications that benefit
from image/video inpainting.
This book compiles leading research on the development of
explainable and interpretable machine learning methods in the
context of computer vision and machine learning. Research progress
in computer vision and pattern recognition has led to a variety of
modeling techniques with almost human-like performance. Although
these models have obtained astounding results, they are limited in
their explainability and interpretability: what is the rationale
behind the decision made? what in the model structure explains its
functioning? Hence, while good performance is a critical required
characteristic for learning machines, explainability and
interpretability capabilities are needed to take learning machines
to the next step to include them in decision support systems
involving human supervision. This book, written by leading
international researchers, addresses key topics of explainability
and interpretability, including the following: * Evaluation and
Generalization in Interpretable Machine Learning * Explanation
Methods in Deep Learning * Learning Functional Causal Models with
Generative Neural Networks * Learning Interpreatable Rules for
Multi-Label Classification * Structuring Neural Networks for More
Explainable Predictions * Generating Post Hoc Rationales of Deep
Visual Classification Decisions * Ensembling Visual Explanations *
Explainable Deep Driving by Visualizing Causal Attention *
Interdisciplinary Perspective on Algorithmic Job Candidate Search *
Multimodal Personality Trait Analysis for Explainable Modeling of
Job Interview Decisions * Inherent Explainability Pattern
Theory-based Video Event Interpretations
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