# Privileged Information for Modeling Affect In The Wild

arXiv: https://arxiv.org/abs/2107.10552 · HTML: https://mlsanity.com/paper/2107.10552/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2021.

## Built on by (most cited first, 8 of 8)

- [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)
- [Supervised Contrastive Learning for Affect Modelling](https://mlsanity.com/paper/2208.12238/) (2022)
- [Learning Task-Independent Game State Representations from Unlabeled Images](https://mlsanity.com/paper/2206.06490/) (2022)
- [Can Large Language Models Capture Video Game Engagement?](https://mlsanity.com/paper/2502.04379/) (2025)
- [Privileged Contrastive Pretraining for Multimodal Affect Modelling](https://mlsanity.com/paper/2508.03729/) (2025)
- [Enhancing Object Detection with Privileged Information: A Model-Agnostic Teacher-Student Approach](https://mlsanity.com/paper/2601.02016/) (2026)
- [PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning](https://mlsanity.com/paper/2510.24058/) (2025)

## Abstract (arXiv)

A key challenge of affective computing research is discovering ways to reliably transfer affect models that are built in the laboratory to real world settings, namely in the wild. The existing gap between in vitro and in vivo affect applications is mainly caused by limitations related to affect sensing including intrusiveness, hardware malfunctions, availability of sensors, but also privacy and security. As a response to these limitations in this paper we are inspired by recent advances in machine learning and introduce the concept of privileged information for operating affect models in the wild. The presence of privileged information enables affect models to be trained across multiple modalities available in a lab setting and ignore modalities that are not available in the wild with no significant drop in their modeling performance. The proposed privileged information framework is tested in a game arousal corpus that contains physiological signals in the form of heart rate and electrodermal activity, game telemetry, and pixels of footage from two dissimilar games that are annotated with arousal traces. By training our arousal models using all modalities (in vitro) and using solely pixels for testing the models (in vivo), we reach levels of accuracy obtained from models that fuse all modalities both for training and testing. The findings of this paper make a decisive step towards realizing affect interaction in the wild.