268 research outputs found
Graph Laplacian for Image Anomaly Detection
Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image
anomaly detection; however, it presents known limitations, namely the
dependence over the image following a multivariate Gaussian model, the
estimation and inversion of a high-dimensional covariance matrix, and the
inability to effectively include spatial awareness in its evaluation. In this
work, a novel graph-based solution to the image anomaly detection problem is
proposed; leveraging the graph Fourier transform, we are able to overcome some
of RXD's limitations while reducing computational cost at the same time. Tests
over both hyperspectral and medical images, using both synthetic and real
anomalies, prove the proposed technique is able to obtain significant gains
over performance by other algorithms in the state of the art.Comment: Published in Machine Vision and Applications (Springer
Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima
Recently, a race towards the simplification of deep networks has begun,
showing that it is effectively possible to reduce the size of these models with
minimal or no performance loss. However, there is a general lack in
understanding why these pruning strategies are effective. In this work, we are
going to compare and analyze pruned solutions with two different pruning
approaches, one-shot and gradual, showing the higher effectiveness of the
latter. In particular, we find that gradual pruning allows access to narrow,
well-generalizing minima, which are typically ignored when using one-shot
approaches. In this work we also propose PSP-entropy, a measure to understand
how a given neuron correlates to some specific learned classes. Interestingly,
we observe that the features extracted by iteratively-pruned models are less
correlated to specific classes, potentially making these models a better fit in
transfer learning approaches
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