Neural Representations Reveal Distinct Modes of Class Fitting in
Residual Convolutional Networks
- URL: http://arxiv.org/abs/2212.00771v1
- Date: Thu, 1 Dec 2022 18:55:58 GMT
- Title: Neural Representations Reveal Distinct Modes of Class Fitting in
Residual Convolutional Networks
- Authors: Micha{\l} Jamro\.z and Marcin Kurdziel
- Abstract summary: We leverage probabilistic models of neural representations to investigate how residual networks fit classes.
We find that classes in the investigated models are not fitted in an uniform way.
We show that the uncovered structure in neural representations correlate with robustness of training examples and adversarial memorization.
- Score: 5.1271832547387115
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We leverage probabilistic models of neural representations to investigate how
residual networks fit classes. To this end, we estimate class-conditional
density models for representations learned by deep ResNets. We then use these
models to characterize distributions of representations across learned classes.
Surprisingly, we find that classes in the investigated models are not fitted in
an uniform way. On the contrary: we uncover two groups of classes that are
fitted with markedly different distributions of representations. These distinct
modes of class-fitting are evident only in the deeper layers of the
investigated models, indicating that they are not related to low-level image
features. We show that the uncovered structure in neural representations
correlate with memorization of training examples and adversarial robustness.
Finally, we compare class-conditional distributions of neural representations
between memorized and typical examples. This allows us to uncover where in the
network structure class labels arise for memorized and standard inputs.
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