Studying classical #CNN architectures such as #VGG and #ResNet, we observed that they could be sensitive to simple rotations... and that incorporating a biologically inspired retinotopic mapping could alleviate this and also bring other nice features observed in the human visual system.

Hear Jean-Nicolas Jérémie aka @jnjer present these results today at the 32nd International Conference on Artificial Neural Networks (#ICANN 2023) in Heraklion, Greece.

The figure below shows the accuracy of different CNNs to the task "is there an animal in the image" - with a classical (Linear) or retinotopic (polar) mapping. Note that VGG may answer confidently the wrong answer for images rotated around 160°...

More in https://laurentperrinet.github.io/publication/jeremie-23-icann/

#DeepLearning is fun sometimes, especially when you play with #ImageNet...

Here is one result of (our modified version of) ResNet which gives a wrong answer compared to the ground truth label, yet it is visually accurate.

A warning for us all that the objective is not just to reach the highest accuracy, more to better understand what is going on...

👉 This was a result obtained by Emmanuel Daucé from Aix Marseille Université, in a joint work with @jnjer and myself.

#NewPaper on ultrafast #visualCategorisation in #biology and #NeuralNetworks 🚀

We used transfer learning to learn to detect if an image contains or not an animal. This a priori simple task is in fact not trivial as the animal can be of any species or in any configuration or pose. This showed as a simple perturbation of the image such as a rotation dropped the accuracy from 99% to a catastrophic 72% at an angle of 45° 😱

However, we found out that data augmentation allowed to get a robust response relative to rotation angle, similarly to what is observed in humans recognition abilities.

All the code is available #openSource at https://laurentperrinet.github.io/publication/jeremie-23-ultra-fast-cat/ (with extensive, reproducible supplementary material).

Check out more of the excellent work from PhD candidate Jean-Nicolas Jérémie !