Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers (1809.03576v1)

Published 4 Sep 2018 in cs.LG, cs.CV, and stat.ML

Abstract: As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-distribution (OOD) inputs while employing these algorithms. In this work, we propose an OOD detection algorithm which comprises of an ensemble of classifiers. We train each classifier in a self-supervised manner by leaving out a random subset of training data as OOD data and the rest as in-distribution (ID) data. We propose a novel margin-based loss over the softmax output which seeks to maintain at least a margin $m$ between the average entropy of the OOD and in-distribution samples. In conjunction with the standard cross-entropy loss, we minimize the novel loss to train an ensemble of classifiers. We also propose a novel method to combine the outputs of the ensemble of classifiers to obtain OOD detection score and class prediction. Overall, our method convincingly outperforms Hendrycks et al.[7] and the current state-of-the-art ODIN[13] on several OOD detection benchmarks.

Citations (239)

Summary

  • The paper proposes an ensemble of classifiers using a novel margin-based entropy loss to improve accuracy in distinguishing OOD and ID data.
  • It demonstrates significant empirical gains on benchmarks like CIFAR-10 and CIFAR-100, notably reducing FPR at 95% TPR compared to methods like ODIN.
  • The methodology and ablation studies underline its potential for enhancing reliability in safety-critical applications such as autonomous driving and medical diagnostics.

An Analysis of Out-of-Distribution Detection Using an Ensemble of Self-Supervised Leave-out Classifiers

The paper "Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers" presents a novel approach to the problem of detecting out-of-distribution (OOD) data in deep learning applications. This concern is particularly pertinent in contexts such as autonomous driving and medical diagnostics, where unexpected inputs can greatly affect system performance. The authors propose a method that leverages an ensemble of classifiers trained in a self-supervised manner to distinguish between in-distribution (ID) data and OOD data, demonstrating a significant advancement over existing methods such as those by Hendrycks et al. and the ODIN framework.

Methodology

The core innovation of this paper revolves around the construction of an ensemble of classifiers, each trained to understand a portion of the training data as ID while considering a strategically excluded set as OOD. This training is guided by a novel margin-based loss function, which aims to enforce a specified margin between the mean entropy of ID and OOD samples. The margin-based entropy loss, when combined with standard cross-entropy loss, serves as an objective that effectively encourages the system to distinguish OOD examples by maintaining higher entropy for these data points relative to ID points, which are expected to produce lower entropy values.

Of particular interest is the approach to partitioning training data into subsets, or "leave-out" groups, for each classifier in the ensemble. The classifiers’ combination method to deduce both OOD detection and class prediction is another critical component. The method achieves a markedly improved performance in OOD detection, as exhibited on benchmark datasets like CIFAR-10 and CIFAR-100, evaluated against various OOD datasets such as TinyImageNet, LSUN, iSUN, and both uniform and Gaussian noise datasets.

Numerical Results and Advancements

A significant contribution of this paper is its empirical superiority over previous techniques. The authors report that their method convincingly outperforms ODIN and other baselines across several metrics, including FPR at 95% TPR, AUROC, AUPR-In and AUPR-Out. For CIFAR-100, the framework achieves an FPR at 95% TPR as low as 8.29 on TinyImageNet when DenseNet-BC is used, a notable improvement over ODIN's results. This advancement is achieved without compromising the classification accuracy of ID samples, which remains competitive compared to other state-of-the-art methods.

Beyond these results, the ablation studies detailed in the paper provide insight into the effectiveness of each component of the method, such as the novel margin-based loss, the effects of different splitting strategies, and input preprocessing parameters.

Implications and Future Directions

The implications of this research are straightforward and profound: it enhances the reliability of systems deployed in safety-critical applications by better handling unexpected inputs. The margin-based loss approach and ensemble of self-supervised classifiers suggest promising directions for achieving robust models capable of high OOD detection accuracy.

Future work could expand this framework to handle computational constraints, potentially by optimizing the ensemble structure or exploring joint parameter sharing techniques among classifiers. Another prospective advance could involve refining the margin-based loss function to eliminate the necessity of accessing OOD data for hyper-parameter tuning, thereby making the model even more applicable in real-world scenarios where OOD data might be entirely unpredictable.

Overall, this research offers substantial contributions toward creating more dependable AI systems, and it sets a foundation for continuing improvements in OOD detection within neural network-based models.