Search from the table of contents of 2.5 million books
Advanced Search (Beta)
Home > Explainable AI: Interpreting, Explaining and Visualizing Deep Learning > Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning |
Wojciech Samek; Grégoire Montavon; Andrea Vedaldi; Lars Kai Hansen; Klaus-Robert Müller
Springer International Publishing
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation
Authors

Seunghoon Hong; Dingdong Yang; Jongwook Choi; Honglak Lee

ARI Id

1665210775737_5378073

Access

Not Available Free

Pages

77-95

DOI

10.1007/978-3-030-28954-6_5

Chapter URL

https://rd.springer.com/chapter/10.1007/978-3-030-28954-6_5

Table of Contents of Book
Showing 1 to 20 of 30 entries
Chapters/HeadingsAuthor(s)PagesInfo
Front Matter
i-xi
Front Matter
1-3
Towards Explainable Artificial Intelligence
Wojciech Samek; Klaus-Robert Müller
5-22
Transparency: Motivations and Challenges
Adrian Weller
23-40
Interpretability in Intelligent Systems – A New Concept?
Lars Kai Hansen; Laura Rieger
41-49
Front Matter
51-53
Understanding Neural Networks via Feature Visualization: A Survey
Anh Nguyen; Jason Yosinski; Jeff Clune
55-76
Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation
Seunghoon Hong; Dingdong Yang; Jongwook Choi; Honglak Lee
77-95
Unsupervised Discrete Representation Learning
Weihua Hu; Takeru Miyato; Seiya Tokui; Eiichi Matsumoto; Masashi Sugiyama
97-119
Towards Reverse-Engineering Black-Box Neural Networks
Seong Joon Oh; Bernt Schiele; Mario Fritz
121-144
Front Matter
145-147
Explanations for Attributing Deep Neural Network Predictions
Ruth Fong; Andrea Vedaldi
149-167
Gradient-Based Attribution Methods
Marco Ancona; Enea Ceolini; Cengiz Öztireli; Markus Gross
169-191
Layer-Wise Relevance Propagation: An Overview
Grégoire Montavon; Alexander Binder; Sebastian Lapuschkin; Wojciech Samek; Klaus-Robert Müller
193-209
Explaining and Interpreting LSTMs
Leila Arras; José Arjona-Medina; Michael Widrich; Grégoire Montavon; Michael Gillhofer; Klaus-Robert Müller et al.
211-238
Front Matter
239-241
Comparing the Interpretability of Deep Networks via Network Dissection
Bolei Zhou; David Bau; Aude Oliva; Antonio Torralba
243-252
Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison
Grégoire Montavon
253-265
The (Un)reliability of Saliency Methods
Pieter-Jan Kindermans; Sara Hooker; Julius Adebayo; Maximilian Alber; Kristof T. Schütt; Sven Dähne et al.
267-280
Front Matter
281-284
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 20 of 30 entries
Similar Books
Similar Chapters
Similar Thesis

Similar News

Similar Articles
Similar Article Headings