Not-So-CLEVR: learning same–different relations strains feedforward neural networks
By Junkyung Kim, Matthew Ricci, Thomas Serre. 2018
105 citations recorded by the source corpus.
The advent of deep learning has recently led to great successes in various engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural network, now approach human accuracy on visual recognition tasks like image classification and face recognition. However, here we will show that feedforward neural networks struggle to learn abstract visual relations that are effortlessly recognized by non-human primates, birds, rodents and even insects. We systematically study the ability of feedforward neural networks to learn to recognize a variety of visual relations and demonstrate that same–different visual relations pose a particular strain on these networks. Networks fail to learn same–different visual relations when stimulus variability makes rote memorization difficult. Further, we show that learning same–different problems becomes trivial for a feedforward network that is fed with perceptually grouped stimuli. This demonstration and the comparative success of biological vision in learning visual relations suggests that feedback mechanisms such as attention, working memory and perceptual grouping may be the key components underlying human-level abstract visual reasoning.
Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- J. Urgen Schmidhuber
- Sjoerd van Steenkiste
- Klaus Greff
- Matthias Bethge
- Wieland Brendel
- Thomas S. A. Wallis
- Christina M. Funke
- Sungjin Ahn
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Andrea Vedaldi
- Ming-Hsuan Yang
- Dacheng Tao
- Xiaogang Wang
- Andrew Zisserman
- Matthias Bethge
Citations in the corpus
- On the Binding Problem in Artificial Neural Networks (2020, background): “(d) Neural networks also struggle to learn visual relations such as whether two shapes are the same or different (Fleuret et al., 2011; Kim et al., 2018).”
- The Notorious Difficulty of Comparing Human and Machine Perception (2020, methodology): “Previous studies (J. Kim et al., 2018; Stabinger et al., 2016) explored how well deep neural networks can learn visual relations by testing them on the Synthetic Visual Reasoning Test (Fleuret et al., 2011).”