55 research outputs found
A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers
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
-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.Comment: 5 pages, 5 figures, to be published in IEEE Signal Processing
Letters, reproducible code https://github.com/idiap/tsoftma
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Modern neural networks are known to give overconfident prediction for
out-of-distribution inputs when deployed in the open world. It is common
practice to leverage a surrogate outlier dataset to regularize the model during
training, and recent studies emphasize the role of uncertainty in designing the
sampling strategy for outlier dataset. However, the OOD samples selected solely
based on predictive uncertainty can be biased towards certain types, which may
fail to capture the full outlier distribution. In this work, we empirically
show that diversity is critical in sampling outliers for OOD detection
performance. Motivated by the observation, we propose a straightforward and
novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse
and informative outliers. Specifically, we cluster the normalized features at
each iteration, and the most informative outlier from each cluster is selected
for model training with absent category loss. With DOS, the sampled outliers
efficiently shape a globally compact decision boundary between ID and OOD data.
Extensive experiments demonstrate the superiority of DOS, reducing the average
FPR95 by up to 25.79% on CIFAR-100 with TI-300K
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