13,164 research outputs found
Understanding Visualization: A formal approach using category theory and semiotics
This article combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely. This article generalizes previous work on the formal characterization of visualization by, inter alia, Ziemkiewicz and Kosara and allows us to formally distinguish properties of the visualization process that previous work does not
Visualizing and Understanding Convolutional Networks
Large Convolutional Network models have recently demonstrated impressive
classification performance on the ImageNet benchmark. However there is no clear
understanding of why they perform so well, or how they might be improved. In
this paper we address both issues. We introduce a novel visualization technique
that gives insight into the function of intermediate feature layers and the
operation of the classifier. We also perform an ablation study to discover the
performance contribution from different model layers. This enables us to find
model architectures that outperform Krizhevsky \etal on the ImageNet
classification benchmark. We show our ImageNet model generalizes well to other
datasets: when the softmax classifier is retrained, it convincingly beats the
current state-of-the-art results on Caltech-101 and Caltech-256 datasets
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Over the last decade, Convolutional Neural Network (CNN) models have been
highly successful in solving complex vision problems. However, these deep
models are perceived as "black box" methods considering the lack of
understanding of their internal functioning. There has been a significant
recent interest in developing explainable deep learning models, and this paper
is an effort in this direction. Building on a recently proposed method called
Grad-CAM, we propose a generalized method called Grad-CAM++ that can provide
better visual explanations of CNN model predictions, in terms of better object
localization as well as explaining occurrences of multiple object instances in
a single image, when compared to state-of-the-art. We provide a mathematical
derivation for the proposed method, which uses a weighted combination of the
positive partial derivatives of the last convolutional layer feature maps with
respect to a specific class score as weights to generate a visual explanation
for the corresponding class label. Our extensive experiments and evaluations,
both subjective and objective, on standard datasets showed that Grad-CAM++
provides promising human-interpretable visual explanations for a given CNN
architecture across multiple tasks including classification, image caption
generation and 3D action recognition; as well as in new settings such as
knowledge distillation.Comment: 17 Pages, 15 Figures, 11 Tables. Accepted in the proceedings of IEEE
Winter Conf. on Applications of Computer Vision (WACV2018). Extended version
is under review at IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers
Picasso is a free open-source (Eclipse Public License) web application
written in Python for rendering standard visualizations useful for analyzing
convolutional neural networks. Picasso ships with occlusion maps and saliency
maps, two visualizations which help reveal issues that evaluation metrics like
loss and accuracy might hide: for example, learning a proxy classification
task. Picasso works with the Tensorflow deep learning framework, and Keras
(when the model can be loaded into the Tensorflow backend). Picasso can be used
with minimal configuration by deep learning researchers and engineers alike
across various neural network architectures. Adding new visualizations is
simple: the user can specify their visualization code and HTML template
separately from the application code.Comment: 9 pages, submission to the Journal of Open Research Software,
github.com/merantix/picass
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