Publisher
Springer Nature Switzerland
Reference19 articles.
1. Cohen, G., Afshar, S., Tapson, J., Van Schaik, A.: EMNIST: extending MNIST to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2921–2926. IEEE (2017)
2. Dapello, J., Marques, T., Schrimpf, M., Geiger, F., Cox, D.D., DiCarlo, J.J.: Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
3. Deng, L.: The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process. Mag. 29(6), 141–142 (2012)
4. Evans, B.D., Malhotra, G., Bowers, J.S.: Biological convolutions improve DNN robustness to noise and generalisation. Neural Netw. 148, 96–110 (2022)
5. Geirhos, R., Janssen, D.H., Schütt, H.H., Rauber, J., Bethge, M., Wichmann, F.A.: Comparing deep neural networks against humans: object recognition when the signal gets weaker. arXiv preprint arXiv:1706.06969 (2017)