results depends on the number of image presentation $T$. In Fig. \[fig:accuracy\], the navy blue curve shows the network test error as a function of $T$. We can see that after 100 stochastic image presentation, the accuracy is nearly equivalent to the use of grayscale images. With eight image presentation, the test accuracy is reduced to $88\%$ instead of $90.1\%$. With a single presentation, the test accuracy is only $76\%$

Adapted Training Procedure {#sec:adaptedtrainig}
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We now try a second strategy, where we train the neural network with binarized stochastic image presentation instead of grayscale images. To do this, during training, we use the conventional BNN training technique of Appendix \[sec:appendix\_training\], but instead of using the normal grayscale Fashion-MNIST images, we use stochastic binarized ones, with the same number of presentation $T$ as will be used during inference. The inference technique then remains identical to the one described in section \[subsec:firstalyer\]. In Fig. \[fig:accuracy\], in cyan color, we plotted the test error rate as a function of the number of presentation of the same image with this scheme. We see that the test accuracy is equivalent to the one obtained with grayscale images for high numbers of image presentation. On 