logo Idiap Research Institute        
 [BibTeX] [Marc21]
A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers
Type of publication: Journal paper
Citation: Antonello_SPL_2020
Publication status: Accepted
Journal: IEEE Signal Processing Letters
Volume: 27
Year: 2020
Month: June
Pages: 1070-1074
DOI: 10.1109/LSP.2020.3001843
Abstract: Neural Network (NN) classifiers can assign extreme probabilities to samples that have not appeared during training (out-of-distribution samples) resulting in erroneous and unreliable predictions. One of the causes for this unwanted behaviour lies in the use of the standard softmax operator which pushes the posterior probabilities to be either zero or unity hence failing to model uncertainty. The statistical derivation of the softmax operator relies on the assumption that the distributions of the latent variables for a given class are Gaussian with known variance. However, it is possible to use different assumptions in the same derivation and attain from other families of distributions as well. This allows derivation of novel operators with more favourable properties. Here, a novel operator is proposed that is derived using t-distributions which are capable of providing a better description of uncertainty. It is shown that classifiers that adopt this novel operator can be more robust to out of distribution samples, often outperforming NNs that use the standard softmax operator. These enhancements can be reached with minimal changes to the NN architecture.
Projects Idiap
Authors Antonello, Niccolò
Garner, Philip N.
Added by: [UNK]
Total mark: 0
  • Antonello_SPL_2020.pdf