130,941 research outputs found
Frequency Dropout: Feature-Level Regularization via Randomized Filtering
Deep convolutional neural networks have shown remarkable performance on
various computer vision tasks, and yet, they are susceptible to picking up
spurious correlations from the training signal. So called `shortcuts' can occur
during learning, for example, when there are specific frequencies present in
the image data that correlate with the output predictions. Both high and low
frequencies can be characteristic of the underlying noise distribution caused
by the image acquisition rather than in relation to the task-relevant
information about the image content. Models that learn features related to this
characteristic noise will not generalize well to new data.
In this work, we propose a simple yet effective training strategy, Frequency
Dropout, to prevent convolutional neural networks from learning
frequency-specific imaging features. We employ randomized filtering of feature
maps during training which acts as a feature-level regularization. In this
study, we consider common image processing filters such as Gaussian smoothing,
Laplacian of Gaussian, and Gabor filtering. Our training strategy is
model-agnostic and can be used for any computer vision task. We demonstrate the
effectiveness of Frequency Dropout on a range of popular architectures and
multiple tasks including image classification, domain adaptation, and semantic
segmentation using both computer vision and medical imaging datasets. Our
results suggest that the proposed approach does not only improve predictive
accuracy but also improves robustness against domain shift.Comment: 15 page
Learning scale-variant and scale-invariant features for deep image classification
Convolutional Neural Networks (CNNs) require large image corpora to be
trained on classification tasks. The variation in image resolutions, sizes of
objects and patterns depicted, and image scales, hampers CNN training and
performance, because the task-relevant information varies over spatial scales.
Previous work attempting to deal with such scale variations focused on
encouraging scale-invariant CNN representations. However, scale-invariant
representations are incomplete representations of images, because images
contain scale-variant information as well. This paper addresses the combined
development of scale-invariant and scale-variant representations. We propose a
multi- scale CNN method to encourage the recognition of both types of features
and evaluate it on a challenging image classification task involving
task-relevant characteristics at multiple scales. The results show that our
multi-scale CNN outperforms single-scale CNN. This leads to the conclusion that
encouraging the combined development of a scale-invariant and scale-variant
representation in CNNs is beneficial to image recognition performance
Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images
We study filter–based feature selection methods for classification of biomedical images. For feature selection, we use two filters — a relevance filter which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. As selection method that combines relevance and redundancy we try out a Hopfield network. We experimentally compare selection methods, running unitary redundancy and relevance filters, against a greedy algorithm with redundancy thresholds [9], the min-redundancy max-relevance integration [8,23,36], and our Hopfield network selection. We conclude that on the whole, Hopfield selection was one of the most successful methods, outperforming min-redundancy max-relevance when\ud
more features are selected
Multiscale Discriminant Saliency for Visual Attention
The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between center and surround
classes. Discriminant power of features for the classification is measured as
mutual information between features and two classes distribution. The estimated
discrepancy of two feature classes very much depends on considered scale
levels; then, multi-scale structure and discriminant power are integrated by
employing discrete wavelet features and Hidden markov tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, saliency value for
each dyadic square at each scale level is computed with discriminant power
principle and the MAP. Finally, across multiple scales is integrated the final
saliency map by an information maximization rule. Both standard quantitative
tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating
the proposed multiscale discriminant saliency method (MDIS) against the
well-know information-based saliency method AIM on its Bruce Database wity
eye-tracking data. Simulation results are presented and analyzed to verify the
validity of MDIS as well as point out its disadvantages for further research
direction.Comment: 16 pages, ICCSA 2013 - BIOCA sessio
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