11 research outputs found
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition
This paper introduces a deep learning enabled generative sensing framework
which integrates low-end sensors with computational intelligence to attain a
high recognition accuracy on par with that attained with high-end sensors. The
proposed generative sensing framework aims at transforming low-end, low-quality
sensor data into higher quality sensor data in terms of achieved classification
accuracy. The low-end data can be transformed into higher quality data of the
same modality or into data of another modality. Different from existing methods
for image generation, the proposed framework is based on discriminative models
and targets to maximize the recognition accuracy rather than a similarity
measure. This is achieved through the introduction of selective feature
regeneration in a deep neural network (DNN). The proposed generative sensing
will essentially transform low-quality sensor data into high-quality
information for robust perception. Results are presented to illustrate the
performance of the proposed framework.Comment: 5 pages, Submitted to IEEE MIPR 201
Toward automatic comparison of visualization techniques: Application to graph visualization
Many end-user evaluations of data visualization techniques have been run
during the last decades. Their results are cornerstones to build efficient
visualization systems. However, designing such an evaluation is always complex
and time-consuming and may end in a lack of statistical evidence and
reproducibility. We believe that modern and efficient computer vision
techniques, such as deep convolutional neural networks (CNNs), may help
visualization researchers to build and/or adjust their evaluation hypothesis.
The basis of our idea is to train machine learning models on several
visualization techniques to solve a specific task. Our assumption is that it is
possible to compare the efficiency of visualization techniques based on the
performance of their corresponding model. As current machine learning models
are not able to strictly reflect human capabilities, including their
imperfections, such results should be interpreted with caution. However, we
think that using machine learning-based pre-evaluation, as a pre-process of
standard user evaluations, should help researchers to perform a more exhaustive
study of their design space. Thus, it should improve their final user
evaluation by providing it better test cases. In this paper, we present the
results of two experiments we have conducted to assess how correlated the
performance of users and computer vision techniques can be. That study compares
two mainstream graph visualization techniques: node-link (\NL) and
adjacency-matrix (\MD) diagrams. Using two well-known deep convolutional neural
networks, we partially reproduced user evaluations from Ghoniem \textit{et al.}
and from Okoe \textit{et al.}. These experiments showed that some user
evaluation results can be reproduced automatically.Comment: 35 pages, 6 figures, 4 table
Angular Visual Hardness
Recent convolutional neural networks (CNNs) have led to impressive performance but often suffer from poor calibration. They tend to be overconfident, with the model confidence not always reflecting the underlying true ambiguity and hardness. In this paper, we propose angular visual hardness (AVH), a score given by the normalized angular distance between the sample feature embedding and the target classifier to measure sample hardness. We validate this score with an in-depth and extensive scientific study, and observe that CNN models with the highest accuracy also have the best AVH scores. This agrees with an earlier finding that state-of-art models improve on the classification of harder examples. We observe that the training dynamics of AVH is vastly different compared to the training loss. Specifically, AVH quickly reaches a plateau for all samples even though the training loss keeps improving. This suggests the need for designing better loss functions that can target harder examples more effectively. We also find that AVH has a statistically significant correlation with human visual hardness. Finally, we demonstrate the benefit of AVH to a variety of applications such as self-training for domain adaptation and domain generalization
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?
Deep neural networks for computer vision are deployed in increasingly
safety-critical and socially-impactful applications, motivating the need to
close the gap in model performance under varied, naturally occurring imaging
conditions. Robustness, ambiguously used in multiple contexts including
adversarial machine learning, refers here to preserving model performance under
naturally-induced image corruptions or alterations.
We perform a systematic review to identify, analyze, and summarize current
definitions and progress towards non-adversarial robustness in deep learning
for computer vision. We find this area of research has received
disproportionately less attention relative to adversarial machine learning, yet
a significant robustness gap exists that manifests in performance degradation
similar in magnitude to adversarial conditions.
Toward developing a more transparent definition of robustness, we provide a
conceptual framework based on a structural causal model of the data generating
process and interpret non-adversarial robustness as pertaining to a model's
behavior on corrupted images corresponding to low-probability samples from the
unaltered data distribution. We identify key architecture-, data augmentation-,
and optimization tactics for improving neural network robustness. This
robustness perspective reveals that common practices in the literature
correspond to causal concepts. We offer perspectives on how future research may
mind this evident and significant non-adversarial robustness gap