# Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition

arXiv: https://arxiv.org/abs/2412.01171 · HTML: https://mlsanity.com/paper/2412.01171/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 55 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016](https://mlsanity.com/paper/2004.06286/) (2020)
- [Balanced Distribution Adaptation for Transfer Learning](https://mlsanity.com/paper/1807.00516/) (2018)
- [Active Learning for Regression Using Greedy Sampling](https://mlsanity.com/paper/1808.04245/) (2018)
- [Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model](https://mlsanity.com/paper/1805.01024/) (2018)
- [Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation](https://mlsanity.com/paper/1810.12782/) (2018)
- [Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression](https://mlsanity.com/paper/1808.04244/) (2018)

## Built on by (most cited first, 3 of 3)

- [CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion Recognition](https://mlsanity.com/paper/2504.09221/) (2025)
- [Cross-Modal Consistency-Guided Active Learning for Affective BCI Systems](https://mlsanity.com/paper/2511.15138/) (2025)
- [Uncertainty-Resilient Multimodal Learning via Consistency-Guided Cross-Modal Transfer](https://mlsanity.com/paper/2511.15741/) (2025)

## Abstract (arXiv)

Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evaluators are usually needed for each affective sample to obtain its ground-truth label, which is expensive. To save the labeling cost, this paper proposes an inconsistency-based active learning approach for cross-task transfer between emotion classification and estimation. Affective norms are utilized as prior knowledge to connect the label spaces of categorical and dimensional emotions. Then, the prediction inconsistency on the two tasks for the unlabeled samples is used to guide sample selection in active learning for the target task. Experiments on within-corpus and cross-corpus transfers demonstrated that cross-task inconsistency could be a very valuable metric in active learning. To our knowledge, this is the first work that utilizes prior knowledge on affective norms and data in a different task to facilitate active learning for a new task, even the two tasks are from different datasets.