Clustering Autoencoder for Outlier Detection and Confidence Calibration
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Solution Overview
Problem
Traditional deep learning classifiers are overconfident and poorly suited to estimate model uncertainty for outliers, anomalies, concept drift, and adversarial attacks, as they ignore features that distinguish in-distribution from out-of-distribution data and are prone to overfitting.
Innovation Solution
A clustering autoencoder model is used for supervised learning, which learns a feature representation that clusters data according to class assignments, providing improved detection of outliers and reducing overfitting by reconstructing input samples and using a latent feature space to determine class probabilities based on distance from a central point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional deep learning classifiers with soft-max function are used, then classification speed and simplicity are improved, but model reliability and accuracy in estimating confidence deteriorate due to overconfidence and poor uncertainty estimation
Solution Approach 1:
The patent segments the classification task into two distinct components: (1) a deep learning classifier for rapid class prediction, and (2) a separate uncertainty estimation module that computes confidence intervals. This segmentation allows each component to specialize - the classifier maintains speed while the uncertainty module provides reliable confidence estimates, resolving the contradiction between productivity and reliability.
Solution Approach 2:
The patent introduces an intermediary uncertainty estimation module that sits between the traditional soft-max classifier and the final decision. This intermediary computes additional statistical measures (confidence intervals, entropy) without significantly impacting classification speed, while dramatically improving reliability by providing calibrated uncertainty estimates that prevent overconfidence.
2Ease of operation
If traditional deep learning classifiers are used, then ease of operation is improved, but detection precision of out-of-distribution data deteriorates due to overfitting and ignoring distinguishing features
Solution Approach 1:
The patent applies preliminary action by training the uncertainty estimation module alongside the main classifier during the training phase. This allows the model to learn both classification and uncertainty calibration simultaneously, improving OOD detection precision without adding significant operational complexity. The uncertainty module is pre-trained to recognize patterns indicative of out-of-distribution data.
3Device complexity
If soft-max function is applied across output nodes, then classification simplicity is improved, but object-generated harmful factors increase due to overconfident class assignments and inability to identify malicious content
Solution Approach 1:
The patent implements feedback by using the uncertainty estimation module to monitor and evaluate the confidence of each classification. When the uncertainty module detects high uncertainty or low confidence (indicating potential malicious or out-of-distribution content), it provides feedback that triggers further verification or rejection of the classification. This feedback loop prevents overconfident incorrect classifications without significantly increasing structural complexity.
Data Source
AI summary
Discussed herein are devices, systems, and methods for classification using a clustering autoencoder. A method can include obtaining content to be classified by the DNN classifier, and operating the DNN classifier to determine a classification of the received content, the DNN classifier including a clustering classification layer that clusters based on a latent feature vector representation of the content, the classification corresponding to one or more clusters that are closest to the latent feature vector providing the classification and a corresponding confidence.