Discriminative Model Verification Using Conditional Input Reconstruction
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Solution Overview
Problem
Existing AI systems face challenges in verifying the predictions of discriminative models when presented with inputs outside their training data distribution or adversarial examples, leading to potential safety issues in applications like security authentication and autonomous driving.
Innovation Solution
A verifier module using a generative model, specifically a conditional variational autoencoder with disentanglement constraints, is employed to reconstruct inputs and evaluate the reliability of discriminative model predictions by estimating the likelihood of the input given the prediction, without requiring reprocessing or retraining of the discriminative model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a discriminative model is used for predictions, then prediction accuracy is improved for training data distribution, but reliability deteriorates for out-of-distribution and adversarial inputs
Solution Approach 1:
A verifier module is introduced as an intermediary between the discriminative model and the final decision. This verifier uses a generative model to assess whether an input belongs to the training data distribution, thereby mediating the reliability assessment without altering the discriminative model's prediction capability. The verifier acts as a gatekeeper that filters out-of-distribution and adversarial inputs.
Solution Approach 2:
The system is segmented into two independent components: a discriminative model for prediction and a verifier module for reliability assessment. The verifier contains a generative model that separately learns the training data distribution. This segmentation allows each component to specialize - the discriminative model optimizes for accuracy while the verifier optimizes for distribution recognition, resolving the contradiction between accuracy and reliability.
2Reliability
If verification is added to verify predictions, then reliability is improved, but device complexity increases
Solution Approach 1:
The verifier module serves multiple functions: it verifies out-of-distribution inputs, detects adversarial examples, and assesses prediction reliability. By consolidating these verification tasks into a single multi-functional module, the system avoids the need for separate verification mechanisms for each type of threat, thereby limiting the increase in complexity while achieving comprehensive reliability improvement.
3Measurement precision
If a generative model is used for verification, then out-of-distribution detection accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The generative model in the verifier is trained in advance on the training data distribution before deployment. During verification, the pre-trained generative model can quickly assess whether new inputs belong to the training distribution without requiring real-time retraining. This preliminary training action separates the computationally intensive training phase from the efficient verification phase, improving detection accuracy while managing training time constraints.
Data Source
AI summary
Verification of discriminative models includes receiving an input; receiving a prediction from a discriminative model for the input; encoding, using an encoder, a latent variable based on the input; decoding, using a decoder, a reconstructed input based on the prediction and the latent variable; and determining, using an anomaly detection module, whether the prediction is reliable based on the input, the reconstructed input, and the latent variable. The encoder and the decoder are jointly trained to maximize an evidence lower bound of the encoder and the decoder. In some embodiments, the encoder and the decoder are further trained using a disentanglement constraint between the prediction and the latent variable. In some embodiments, the encoder and the decoder are further trained without using inputs that are out of a distribution of inputs used to train the discriminative model or that are adversarial to the discriminative model.