# A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer

arXiv: https://arxiv.org/abs/2110.08598 · HTML: https://mlsanity.com/paper/2110.08598/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 28 of 28)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [Relational Knowledge Distillation](https://mlsanity.com/paper/1904.05068/) (2019)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Similarity-Preserving Knowledge Distillation](https://mlsanity.com/paper/1907.09682/) (2019)
- [A Comprehensive Overhaul of Feature Distillation](https://mlsanity.com/paper/1904.01866/) (2019)
- [Variational Continual Learning](https://mlsanity.com/paper/1710.10628/) (2017)
- [Variational Information Distillation for Knowledge Transfer](https://mlsanity.com/paper/1904.05835/) (2019)
- [Correlation Congruence for Knowledge Distillation](https://mlsanity.com/paper/1904.01802/) (2019)
- [Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons](https://mlsanity.com/paper/1811.03233/) (2018)
- [Learning Deep Representations with Probabilistic Knowledge Transfer](https://mlsanity.com/paper/1803.10837/) (2018)
- [Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review](https://mlsanity.com/paper/1611.00740/) (2016)
- [A multi-device dataset for urban acoustic scene classification](https://mlsanity.com/paper/1807.09840/) (2018)
- [Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions](https://mlsanity.com/paper/2005.14623/) (2020)
- [Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation](https://mlsanity.com/paper/2007.08389/) (2020)
- [L-Vector: Neural Label Embedding for Domain Adaptation](https://mlsanity.com/paper/2004.13480/) (2020)
- [A Two-Stage Approach to Device-Robust Acoustic Scene Classification](https://mlsanity.com/paper/2011.01447/) (2020)
- [Unsupervised adversarial domain adaptation for acoustic scene classification](https://mlsanity.com/paper/1808.05777/) (2018)
- [Maximum a Posteriori Adaptation of Network Parameters in Deep Models](https://mlsanity.com/paper/1503.02108/) (2015)
- [Scalable Factorized Hierarchical Variational Autoencoder Training](https://mlsanity.com/paper/1804.03201/) (2018)
- [Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification](https://mlsanity.com/paper/2008.00110/) (2020)
- [Variational Information Bottleneck for Effective Low-resource Audio Classification](https://mlsanity.com/paper/2107.04803/) (2021)
- [Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping](https://mlsanity.com/paper/2011.02955/) (2020)

## Built on by (most cited first, 2 of 2)

- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models](https://mlsanity.com/paper/2603.23783/) (2026)

## Abstract (arXiv)

We propose a variational Bayesian (VB) approach to learning distributions of latent variables in deep neural network (DNN) models for cross-domain knowledge transfer, to address acoustic mismatches between training and testing conditions. Instead of carrying out point estimation in conventional maximum a posteriori estimation with a risk of having a curse of dimensionality in estimating a huge number of model parameters, we focus our attention on estimating a manageable number of latent variables of DNNs via a VB inference framework. To accomplish model transfer, knowledge learnt from a source domain is encoded in prior distributions of latent variables and optimally combined, in a Bayesian sense, with a small set of adaptation data from a target domain to approximate the corresponding posterior distributions. Experimental results on device adaptation in acoustic scene classification show that our proposed VB approach can obtain good improvements on target devices, and consistently outperforms 13 state-of-the-art knowledge transfer algorithms.