# Resource-Constrained Affect Modelling via Variance Regularisation Pruning

arXiv: https://arxiv.org/abs/2605.27479 · HTML: https://mlsanity.com/paper/2605.27479/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 31 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 24 of 24)

- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [Out-of-Distribution Generalization via Risk Extrapolation (REx)](https://mlsanity.com/paper/2003.00688/) (2020)
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://mlsanity.com/paper/1707.06168/) (2017)
- [Importance Estimation for Neural Network Pruning](https://mlsanity.com/paper/1906.10771/) (2019)
- [Pruning neural networks without any data by iteratively conserving synaptic flow](https://mlsanity.com/paper/2006.05467/) (2020)
- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Methods for Pruning Deep Neural Networks](https://mlsanity.com/paper/2011.00241/) (2020)
- [Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)](https://mlsanity.com/paper/1903.12094/) (2019)
- [The Arousal video Game AnnotatIoN (AGAIN) Dataset](https://mlsanity.com/paper/2104.02643/) (2021)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [PAGAN: Video Affect Annotation Made Easy](https://mlsanity.com/paper/1907.01008/) (2019)
- [From the Lab to the Wild: Affect Modeling via Privileged Information](https://mlsanity.com/paper/2305.10919/) (2023)
- [Affective Game Computing: A Survey](https://mlsanity.com/paper/2309.14104/) (2023)
- [The Invariant Ground Truth of Affect](https://mlsanity.com/paper/2210.07630/) (2022)
- [Knowing Your Annotator: Rapidly Testing the Reliability of Affect Annotation](https://mlsanity.com/paper/2308.16029/) (2023)
- [GameVibe: A Multimodal Affective Game Corpus](https://mlsanity.com/paper/2407.12787/) (2024)
- [Across-Game Engagement Modelling via Few-Shot Learning](https://mlsanity.com/paper/2409.13002/) (2024)
- [Privileged Contrastive Pretraining for Multimodal Affect Modelling](https://mlsanity.com/paper/2508.03729/) (2025)
- [Learning Annotation Consensus for Continuous Emotion Recognition](https://mlsanity.com/paper/2505.21196/) (2025)

## Abstract (arXiv)

Affective computing systems are increasingly embedded in pervasive and interactive environments, such as adaptive games, assistive technologies, and resource-constrained platforms, where computational efficiency must be balanced with reliability across diverse users. Model pruning offers an effective way to reduce computational demands, yet existing approaches typically optimise for sparsity alone, without accounting for how parameter removal impacts robustness across individuals. In this work, we introduce Variance-Regularised Pruning (VR), a pruning framework that explicitly incorporates cross-participant stability into the sparsification process. Rather than relying solely on average prediction error, VR evaluates each connection based on its joint contribution to both prediction accuracy and variability across users, prioritising parameters that remain reliable under distributional differences. We evaluate the proposed approach on the AGAIN dataset, which includes arousal annotations collected across nine affect-eliciting game environments. Experimental results demonstrate that VR maintains competitive Concordance Correlation Coefficient (CCC) performance even at 80% sparsity without additional fine-tuning, highlighting its suitability for deployment in real-world, resource-limited affect-aware systems. Overall, the proposed framework supports the development of compact, robust affective models that can operate reliably in real-world interactive environments.