Matrices of Euclidean distances for pixel gray-levels (A), the IT neurons (B), and 5 layers of the trained and untrained versions of 2 deep CNNs (C). Note that the dissimilarity matrices are by definition symmetric about the diagonal of zeros, which is plotted in white color. The stimulus groups are indicated in (A) as in Fig 1 and the CNN layers in (C) have the same terminology as in Fig 2. The matrices have been separately normalized and are plotted in percentile units, following [23]. Dissimilarities increase from blue to yellow.</p
In the traditional way of learning from examples of objects the classifiers are built in a feature s...
(A) Mean response modulations of IT neurons for the shape groups R, IC, ISC, ISS, “ISCa vs ISSa” and...
<p>Top row: cartoon of a divisive normalization model that accounts for surround modulation of V1 re...
(A, B). Gray curves show the Pearson correlation coefficients between the mean neural distances and ...
<p>The behavioral distance between pairs of tasks follows a simple rule: darker the square, smaller ...
<p>Example images are shown on the left. Activations for each of the eight layers of a pre-trained d...
<p>We used feature representations, extracted with the four Spatiotemporal Convolutional Neural Netw...
Dissimilarities for groups R, IC, ISC, ISS, “ISCa vs ISSa”, “ISCb vs ISSb” of selected trained (left...
(A) Spearman rank correlation coefficients between IT and peak CNN layer similarities are shown for ...
<p>The IT RDMs (black frames) for human (<b>A</b>) and monkey (<b>B</b>) and the seven most highly c...
Application-specific dissimilarity functions can be used for learning from a set of objects represen...
The visual cortex is able to extract disparity information through the use of binocular cells. This ...
The real-time estimation of the distance of objects from an observer is a critical issue in several ...
In many real-world applications concerning pattern recognition techniques, it is of utmost importanc...
(A) Spearman rank correlation coefficients between IT and peak CNN layer similarities are shown for ...
In the traditional way of learning from examples of objects the classifiers are built in a feature s...
(A) Mean response modulations of IT neurons for the shape groups R, IC, ISC, ISS, “ISCa vs ISSa” and...
<p>Top row: cartoon of a divisive normalization model that accounts for surround modulation of V1 re...
(A, B). Gray curves show the Pearson correlation coefficients between the mean neural distances and ...
<p>The behavioral distance between pairs of tasks follows a simple rule: darker the square, smaller ...
<p>Example images are shown on the left. Activations for each of the eight layers of a pre-trained d...
<p>We used feature representations, extracted with the four Spatiotemporal Convolutional Neural Netw...
Dissimilarities for groups R, IC, ISC, ISS, “ISCa vs ISSa”, “ISCb vs ISSb” of selected trained (left...
(A) Spearman rank correlation coefficients between IT and peak CNN layer similarities are shown for ...
<p>The IT RDMs (black frames) for human (<b>A</b>) and monkey (<b>B</b>) and the seven most highly c...
Application-specific dissimilarity functions can be used for learning from a set of objects represen...
The visual cortex is able to extract disparity information through the use of binocular cells. This ...
The real-time estimation of the distance of objects from an observer is a critical issue in several ...
In many real-world applications concerning pattern recognition techniques, it is of utmost importanc...
(A) Spearman rank correlation coefficients between IT and peak CNN layer similarities are shown for ...
In the traditional way of learning from examples of objects the classifiers are built in a feature s...
(A) Mean response modulations of IT neurons for the shape groups R, IC, ISC, ISS, “ISCa vs ISSa” and...
<p>Top row: cartoon of a divisive normalization model that accounts for surround modulation of V1 re...