# Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition

arXiv: https://arxiv.org/abs/2407.09521 · HTML: https://mlsanity.com/paper/2407.09521/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal distillation, one of 12 directions in Knowledge distillation.
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Placed in

- Knowledge distillation / [Multimodal distillation](https://mlsanity.com/distill/multimodal-distillation/)

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting](https://mlsanity.com/paper/2202.11946/) (2022)
- [TransFER: Learning Relation-aware Facial Expression Representations with Transformers](https://mlsanity.com/paper/2108.11116/) (2021)
- [Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition](https://mlsanity.com/paper/2104.05160/) (2021)
- [Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation](https://mlsanity.com/paper/2304.05627/) (2023)
- [MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition](https://mlsanity.com/paper/1911.09784/) (2019)
- [Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks](https://mlsanity.com/paper/2002.06199/) (2020)
- [Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition](https://mlsanity.com/paper/2103.13372/) (2021)
- [Event-Driven Learning for Spiking Neural Networks](https://mlsanity.com/paper/2403.00270/) (2024)
- [Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons](https://mlsanity.com/paper/1911.08261/) (2019)
- [Eyemotion: Classifying facial expressions in VR using eye-tracking cameras](https://mlsanity.com/paper/1707.07204/) (2017)
- [Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks](https://mlsanity.com/paper/2304.09500/) (2023)
- [Temporal Knowledge Sharing enable Spiking Neural Network Learning from Past and Future](https://mlsanity.com/paper/2304.06540/) (2023)
- [Event-Enhanced Multi-Modal Spiking Neural Network for Dynamic Obstacle Avoidance](https://mlsanity.com/paper/2310.02361/) (2023)
- [Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning](https://mlsanity.com/paper/2008.05563/) (2020)
- [Recognizing Facial Expressions of Occluded Faces using Convolutional Neural Networks](https://mlsanity.com/paper/1911.04852/) (2019)
- [In the Blink of an Eye: Event-based Emotion Recognition](https://mlsanity.com/paper/2310.04043/) (2023)

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

- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [Dynamic Weight Adaptation in Spiking Neural Networks Inspired by Biological Homeostasis](https://mlsanity.com/paper/2511.17563/) (2025)
- [Multi-modal Collaborative Optimization and Expansion Network for Event-assisted Single-eye Expression Recognition](https://mlsanity.com/paper/2505.12007/) (2025)

## Abstract (arXiv)

We introduce a novel multimodality synergistic knowledge distillation scheme tailored for efficient single-eye motion recognition tasks. This method allows a lightweight, unimodal student spiking neural network (SNN) to extract rich knowledge from an event-frame multimodal teacher network. The core strength of this approach is its ability to utilize the ample, coarser temporal cues found in conventional frames for effective emotion recognition. Consequently, our method adeptly interprets both temporal and spatial information from the conventional frame domain, eliminating the need for specialized sensing devices, e.g., event-based camera. The effectiveness of our approach is thoroughly demonstrated using both existing and our compiled single-eye emotion recognition datasets, achieving unparalleled performance in accuracy and efficiency over existing state-of-the-art methods.