# Using Human-like Mechanism to Weaken Effect of Pre-training Weight Bias in Face-Recognition Convolutional Neural Network

arXiv: https://arxiv.org/abs/2310.13674 · HTML: https://mlsanity.com/paper/2310.13674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 69 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 21 of 21)

- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Inductive Biases for Deep Learning of Higher-Level Cognition](https://mlsanity.com/paper/2011.15091/) (2020)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning](https://mlsanity.com/paper/1605.08104/) (2016)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [PlaNet - Photo Geolocation with Convolutional Neural Networks](https://mlsanity.com/paper/1602.05314/) (2016)
- [Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future](https://mlsanity.com/paper/2001.07092/) (2020)
- [Unsupervised deep learning identifies semantic disentanglement in single inferotemporal neurons](https://mlsanity.com/paper/2006.14304/) (2020)
- [Analyzing biological and artificial neural networks: challenges with opportunities for synergy?](https://mlsanity.com/paper/1810.13373/) (2018)
- [Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge](https://mlsanity.com/paper/2105.01445/) (2021)

## Abstract (arXiv)

Convolutional neural network (CNN), as an important model in artificial intelligence, has been widely used and studied in different disciplines. The computational mechanisms of CNNs are still not fully revealed due to the their complex nature. In this study, we focused on 4 extensively studied CNNs (AlexNet, VGG11, VGG13, and VGG16) which has been analyzed as human-like models by neuroscientists with ample evidence. We trained these CNNs to emotion valence classification task by transfer learning. Comparing their performance with human data, the data unveiled that these CNNs would partly perform as human does. We then update the object-based AlexNet using self-attention mechanism based on neuroscience and behavioral data. The updated FE-AlexNet outperformed all the other tested CNNs and closely resembles human perception. The results further unveil the computational mechanisms of these CNNs. Moreover, this study offers a new paradigm to better understand and improve CNN performance via human data.