# Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

arXiv: https://arxiv.org/abs/1911.07421 · HTML: https://mlsanity.com/paper/1911.07421/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 25 of 25)

- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate](https://mlsanity.com/paper/2107.13469/) (2021)
- [Learning to Give Checkable Answers with Prover-Verifier Games](https://mlsanity.com/paper/2108.12099/) (2021)
- [ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training](https://mlsanity.com/paper/2206.02288/) (2022)
- [Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis](https://mlsanity.com/paper/2106.12499/) (2021)
- [Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis](https://mlsanity.com/paper/2101.00318/) (2021)
- [An evaluation of word-level confidence estimation for end-to-end automatic speech recognition](https://mlsanity.com/paper/2101.05525/) (2021)
- [Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference](https://mlsanity.com/paper/2107.10931/) (2021)
- [Energy-constrained Self-training for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2101.00316/) (2021)
- [Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation](https://mlsanity.com/paper/2209.07910/) (2022)
- [Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation](https://mlsanity.com/paper/2107.13467/) (2021)
- [Identity-aware Facial Expression Recognition in Compressed Video](https://mlsanity.com/paper/2101.00317/) (2021)
- [Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation](https://mlsanity.com/paper/2506.17442/) (2025)
- [Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives](https://mlsanity.com/paper/2201.10989/) (2022)
- [Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer](https://mlsanity.com/paper/2309.14586/) (2023)
- [Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels](https://mlsanity.com/paper/2504.19816/) (2025)
- [Subtype-Aware Dynamic Unsupervised Domain Adaptation](https://mlsanity.com/paper/2208.07754/) (2022)
- [Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model](https://mlsanity.com/paper/2208.12885/) (2022)
- [Deep 3D-CNN for Depression Diagnosis with Facial Video Recording of Self-Rating Depression Scale Questionnaire](https://mlsanity.com/paper/2107.10712/) (2021)
- [Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator](https://mlsanity.com/paper/2206.02284/) (2022)
- [Automated interpretation of congenital heart disease from multi-view echocardiograms](https://mlsanity.com/paper/2311.18788/) (2023)
- [Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI](https://mlsanity.com/paper/2402.06984/) (2024)
- [Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins](https://mlsanity.com/paper/2509.12982/) (2025)
- [DOI: Divergence-based Out-of-Distribution Indicators via Deep Generative Models](https://mlsanity.com/paper/2108.05509/) (2021)
- [Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques](https://mlsanity.com/paper/2406.13441/) (2024)

## Abstract (arXiv)

AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework -- deep verifier networks (DVN) to verify the inputs and outputs of deep discriminative models with deep generative models. Our proposed model is based on conditional variational auto-encoders with disentanglement constraints. We give both intuitive and theoretical justifications of the model. Our verifier network is trained independently with the prediction model, which eliminates the need of retraining the verifier network for a new model. We test the verifier network on out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation. We achieve state-of-the-art results in all of these problems.