2,298 research outputs found
Discriminative Deep Feature Visualization for Explainable Face Recognition
Despite the huge success of deep convolutional neural networks in face
recognition (FR) tasks, current methods lack explainability for their
predictions because of their "black-box" nature. In recent years, studies have
been carried out to give an interpretation of the decision of a deep FR system.
However, the affinity between the input facial image and the extracted deep
features has not been explored. This paper contributes to the problem of
explainable face recognition by first conceiving a face reconstruction-based
explanation module, which reveals the correspondence between the deep feature
and the facial regions. To further interpret the decision of an FR model, a
novel visual saliency explanation algorithm has been proposed. It provides
insightful explanation by producing visual saliency maps that represent similar
and dissimilar regions between input faces. A detailed analysis has been
presented for the generated visual explanation to show the effectiveness of the
proposed method
Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization
Feature visualization has gained substantial popularity, particularly after
the influential work by Olah et al. in 2017, which established it as a crucial
tool for explainability. However, its widespread adoption has been limited due
to a reliance on tricks to generate interpretable images, and corresponding
challenges in scaling it to deeper neural networks. Here, we describe MACO, a
simple approach to address these shortcomings. The main idea is to generate
images by optimizing the phase spectrum while keeping the magnitude constant to
ensure that generated explanations lie in the space of natural images. Our
approach yields significantly better results (both qualitatively and
quantitatively) and unlocks efficient and interpretable feature visualizations
for large state-of-the-art neural networks. We also show that our approach
exhibits an attribution mechanism allowing us to augment feature visualizations
with spatial importance. We validate our method on a novel benchmark for
comparing feature visualization methods, and release its visualizations for all
classes of the ImageNet dataset on https://serre-lab.github.io/Lens/.
Overall, our approach unlocks, for the first time, feature visualizations for
large, state-of-the-art deep neural networks without resorting to any
parametric prior image model
SeqVISTA: a graphical tool for sequence feature visualization and comparison
BACKGROUND: Many readers will sympathize with the following story. You are viewing a gene sequence in Entrez, and you want to find whether it contains a particular sequence motif. You reach for the browser's "find in page" button, but those darn spaces every 10 bp get in the way. And what if the motif is on the opposite strand? Subsequently, your favorite sequence analysis software informs you that there is an interesting feature at position 13982–14013. By painstakingly counting the 10 bp blocks, you are able to examine the sequence at this location. But now you want to see what other features have been annotated close by, and this information is buried several screenfuls higher up the web page. RESULTS: SeqVISTA presents a holistic, graphical view of features annotated on nucleotide or protein sequences. This interactive tool highlights the residues in the sequence that correspond to features chosen by the user, and allows easy searching for sequence motifs or extraction of particular subsequences. SeqVISTA is able to display results from diverse sequence analysis tools in an integrated fashion, and aims to provide much-needed unity to the bioinformatics resources scattered around the Internet. Our viewer may be launched on a GenBank record by a single click of a button installed in the web browser. CONCLUSION: SeqVISTA allows insights to be gained by viewing the totality of sequence annotations and predictions, which may be more revealing than the sum of their parts. SeqVISTA runs on any operating system with a Java 1.4 virtual machine. It is freely available to academic users at
Targeted Background Removal Creates Interpretable Feature Visualizations
Feature visualization is used to visualize learned features for black box
machine learning models. Our approach explores an altered training process to
improve interpretability of the visualizations. We argue that by using
background removal techniques as a form of robust training, a network is forced
to learn more human recognizable features, namely, by focusing on the main
object of interest without any distractions from the background. Four different
training methods were used to verify this hypothesis. The first used unmodified
pictures. The second used a black background. The third utilized Gaussian noise
as the background. The fourth approach employed a mix of background removed
images and unmodified images. The feature visualization results show that the
background removed images reveal a significant improvement over the baseline
model. These new results displayed easily recognizable features from their
respective classes, unlike the model trained on unmodified data
Protter: interactive protein feature visualization and integration with experimental proteomic data
Summary: The ability to integrate and visualize experimental proteomic evidence in the context of rich protein feature annotations represents an unmet need of the proteomics community. Here we present Protter, a web-based tool that supports interactive protein data analysis and hypothesis generation by visualizing both annotated sequence features and experimental proteomic data in the context of protein topology. Protter supports numerous proteomic file formats and automatically integrates a variety of reference protein annotation sources, which can be readily extended via modular plug-ins. A built-in export function produces publication-quality customized protein illustrations, also for large datasets. Visualizations of surfaceome datasets show the specific utility of Protter for the integrated visual analysis of membrane proteins and peptide selection for targeted proteomics. Availability and implementation: The Protter web application is available at http://wlab.ethz.ch/protter. Source code and installation instructions are available at http://ulo.github.io/Protter/. Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics onlin
Saliency-driven Class Impressions for Feature Visualization of Deep Neural Networks
In this paper, we propose a data-free method of extracting Impressions of
each class from the classifier's memory. The Deep Learning regime empowers
classifiers to extract distinct patterns (or features) of a given class from
training data, which is the basis on which they generalize to unseen data.
Before deploying these models on critical applications, it is advantageous to
visualize the features considered to be essential for classification. Existing
visualization methods develop high confidence images consisting of both
background and foreground features. This makes it hard to judge what the
crucial features of a given class are. In this work, we propose a
saliency-driven approach to visualize discriminative features that are
considered most important for a given task. Another drawback of existing
methods is that confidence of the generated visualizations is increased by
creating multiple instances of the given class. We restrict the algorithm to
develop a single object per image, which helps further in extracting features
of high confidence and also results in better visualizations. We further
demonstrate the generation of negative images as naturally fused images of two
or more classes.Comment: ICIP 202
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