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Contextual Semantic Interpretability
- Source :
- ACCV (Asian Conference on Computer Vision), ACCV (Asian Conference on Computer Vision), Dec 2020, Kyoto, France, Computer Vision – ACCV 2020 ISBN: 9783030695378, ACCV (4), Computer Vision – ACCV 2020-15th Asian Conference on Computer Vision, 2020, Revised Selected Papers, Computer Vision – ACCV 2020-15th Asian Conference on Computer Vision, 2020, Revised Selected Papers. Cham: Springer
- Publication Year :
- 2021
-
Abstract
- International audience; Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in the neurons and can be made explicit by teaching the model to recognize semantically interpretable attributes that are present in the scene. We call such an intermediate layer a \emph{semantic bottleneck}. Once the attributes are learned, they can be re-combined to reach the final decision and provide both an accurate prediction and an explicit reasoning behind the CNN decision. In this paper, we look into semantic bottlenecks that capture context: we want attributes to be in groups of a few meaningful elements and participate jointly to the final decision. We use a two-layer semantic bottleneck that gathers attributes into interpretable, sparse groups, allowing them contribute differently to the final output depending on the context. We test our contextual semantic interpretable bottleneck (CSIB) on the task of landscape scenicness estimation and train the semantic interpretable bottleneck using an auxiliary database (SUN Attributes). Our model yields in predictions as accurate as a non-interpretable baseline when applied to a real-world test set of Flickr images, all while providing clear and interpretable explanations for each prediction.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Artificial Intelligence
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Context (language use)
02 engineering and technology
computer.software_genre
Convolutional neural network
Bottleneck
Machine Learning (cs.LG)
Task (project management)
03 medical and health sciences
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Laboratory of Geo-information Science and Remote Sensing
Interpretable deep learning
0202 electrical engineering, electronic engineering, information engineering
Ecosystem services
Interpretability
Laboratorium voor Geo-informatiekunde en Remote Sensing
Representation (mathematics)
Baseline (configuration management)
030304 developmental biology
0303 health sciences
business.industry
Deep learning
PE&RC
Artificial Intelligence (cs.AI)
Test set
Explainable AI
020201 artificial intelligence & image processing
Lanscape scenicness
Artificial intelligence
business
computer
Sparsity
Natural language processing
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-030-69537-8
- ISBNs :
- 9783030695378
- Database :
- OpenAIRE
- Journal :
- ACCV (Asian Conference on Computer Vision), ACCV (Asian Conference on Computer Vision), Dec 2020, Kyoto, France, Computer Vision – ACCV 2020 ISBN: 9783030695378, ACCV (4), Computer Vision – ACCV 2020-15th Asian Conference on Computer Vision, 2020, Revised Selected Papers, Computer Vision – ACCV 2020-15th Asian Conference on Computer Vision, 2020, Revised Selected Papers. Cham: Springer
- Accession number :
- edsair.doi.dedup.....729eff680167bc7e5e879ab02157bcb6