157,673 research outputs found
Self-supervised out-of-distribution detection in wireless capsule endoscopy images.
While deep learning has displayed excellent performance in a broad spectrum of application areas, neural networks still struggle to recognize what they have not seen, i.e., out-of-distribution (OOD) inputs. In the medical field, building robust models that are able to detect OOD images is highly critical, as these rare images could show diseases or anomalies that should be detected. In this study, we use wireless capsule endoscopy (WCE) images to present a novel patch-based self-supervised approach comprising three stages. First, we train a triplet network to learn vector representations of WCE image patches. Second, we cluster the patch embeddings to group patches in terms of visual similarity. Third, we use the cluster assignments as pseudolabels to train a patch classifier and use the Out-of-Distribution Detector for Neural Networks (ODIN) for OOD detection. The system has been tested on the Kvasir-capsule, a publicly released WCE dataset. Empirical results show an OOD detection improvement compared to baseline methods. Our method can detect unseen pathologies and anomalies such as lymphangiectasia, foreign bodies and blood with > 0.6. This work presents an effective solution for OOD detection models without needing labeled images
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
Out-of-distribution (OOD) detection is a critical requirement for the
deployment of deep neural networks. This paper introduces the HEAT model, a new
post-hoc OOD detection method estimating the density of in-distribution (ID)
samples using hybrid energy-based models (EBM) in the feature space of a
pre-trained backbone. HEAT complements prior density estimators of the ID
density, e.g. parametric models like the Gaussian Mixture Model (GMM), to
provide an accurate yet robust density estimation. A second contribution is to
leverage the EBM framework to provide a unified density estimation and to
compose several energy terms. Extensive experiments demonstrate the
significance of the two contributions. HEAT sets new state-of-the-art OOD
detection results on the CIFAR-10 / CIFAR-100 benchmark as well as on the
large-scale Imagenet benchmark. The code is available at:
https://github.com/MarcLafon/heatood
Density Uncertainty Layers for Reliable Uncertainty Estimation
Assessing the predictive uncertainty of deep neural networks is crucial for
safety-related applications of deep learning. Although Bayesian deep learning
offers a principled framework for estimating model uncertainty, the approaches
that are commonly used to approximate the posterior often fail to deliver
reliable estimates of predictive uncertainty. In this paper we propose a novel
criterion for predictive uncertainty, that a model's predictive variance should
be grounded in the empirical density of the input. It should produce higher
uncertainty for inputs that are improbable in the training data and lower
uncertainty for those inputs that are more probable. To operationalize this
criterion, we develop the density uncertainty layer, an architectural element
for a stochastic neural network that guarantees that the density uncertain
criterion is satisfied. We study neural networks with density uncertainty
layers on the CIFAR-10 and CIFAR-100 uncertainty benchmarks. Compared to
existing approaches, we find that density uncertainty layers provide reliable
uncertainty estimates and robust out-of-distribution detection performance
Signal detection in extracellular neural ensemble recordings using higher criticism
Information processing in the brain is conducted by a concerted action of
multiple neural populations. Gaining insights in the organization and dynamics
of such populations can best be studied with broadband intracranial recordings
of so-called extracellular field potential, reflecting neuronal spiking as well
as mesoscopic activities, such as waves, oscillations, intrinsic large
deflections, and multiunit spiking activity. Such signals are critical for our
understanding of how neuronal ensembles encode sensory information and how such
information is integrated in the large networks underlying cognition. The
aforementioned principles are now well accepted, yet the efficacy of extracting
information out of the complex neural data, and their employment for improving
our understanding of neural networks, critically depends on the mathematical
processing steps ranging from simple detection of action potentials in noisy
traces - to fitting advanced mathematical models to distinct patterns of the
neural signal potentially underlying intra-processing of information, e.g.
interneuronal interactions. Here, we present a robust strategy for detecting
signals in broadband and noisy time series such as spikes, sharp waves and
multi-unit activity data that is solely based on the intrinsic statistical
distribution of the recorded data. By using so-called higher criticism - a
second-level significance testing procedure comparing the fraction of observed
significances to an expected fraction under the global null - we are able to
detect small signals in correlated noisy time-series without prior filtering,
denoising or data regression. Results demonstrate the efficiency and
reliability of the method and versatility over a wide range of experimental
conditions and suggest the appropriateness of higher criticism to characterize
neuronal dynamics without prior manipulation of the data
Uncertainty-Estimation with Normalized Logits for Out-of-Distribution Detection
Out-of-distribution (OOD) detection is critical for preventing deep learning
models from making incorrect predictions to ensure the safety of artificial
intelligence systems. Especially in safety-critical applications such as
medical diagnosis and autonomous driving, the cost of incorrect decisions is
usually unbearable. However, neural networks often suffer from the
overconfidence issue, making high confidence for OOD data which are never seen
during training process and may be irrelevant to training data, namely
in-distribution (ID) data. Determining the reliability of the prediction is
still a difficult and challenging task. In this work, we propose
Uncertainty-Estimation with Normalized Logits (UE-NL), a robust learning method
for OOD detection, which has three main benefits. (1) Neural networks with
UE-NL treat every ID sample equally by predicting the uncertainty score of
input data and the uncertainty is added into softmax function to adjust the
learning strength of easy and hard samples during training phase, making the
model learn robustly and accurately. (2) UE-NL enforces a constant vector norm
on the logits to decouple the effect of the increasing output norm from
optimization process, which causes the overconfidence issue to some extent. (3)
UE-NL provides a new metric, the magnitude of uncertainty score, to detect OOD
data. Experiments demonstrate that UE-NL achieves top performance on common OOD
benchmarks and is more robust to noisy ID data that may be misjudged as OOD
data by other methods.Comment: 7 pages, 1 figure, 7 tables, preprin
Close Category Generalization for Out-of-Distribution Classification
Out-of-distribution generalization is a core challenge in machine learning.
We introduce and propose a solution to a new type of out-of-distribution
evaluation, which we call close category generalization. This task specifies
how a classifier should extrapolate to unseen classes by considering a
bi-criteria objective: (i) on in-distribution examples, output the correct
label, and (ii) on out-of-distribution examples, output the label of the
nearest neighbor in the training set. In addition to formalizing this problem,
we present a new training algorithm to improve the close category
generalization of neural networks. We compare to many baselines, including
robust algorithms and out-of-distribution detection methods, and we show that
our method has better or comparable close category generalization. Then, we
investigate a related representation learning task, and we find that performing
well on close category generalization correlates with learning a good
representation of an unseen class and with finding a good initialization for
few-shot learning. The code is available at
https://github.com/yangarbiter/close-category-generalizatio
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