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Found 6 papers, 4 papers with code
Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds
Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studies or global normative approaches.
Adversarially trained neural representations may already be as robust as corresponding biological neural representations
Visual systems of primates are the gold standard of robust perception.
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception
Adversarial examples are often cited by neuroscientists and machine learning researchers as an example of how computational models diverge from biological sensory systems.
Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs
Finally, we show that using distillation, it is possible to partially compress the knowledge in the ensemble model into a single model with a V1 front-end.
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
Current state-of-the-art object recognition models are largely based on convolutional neural network (CNN) architectures, which are loosely inspired by the primate visual system.
On the Information Bottleneck Theory of Deep Learning
The practical successes of deep neural networks have not been matched by theoretical progress that satisfyingly explains their behavior.
