Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The role of invariance in spectral complexity-based generalization
  bounds   Deep convolutional neural networks (CNNs) have been shown to be able to fit a
random labeling over data while still being able to generalize well for normal
labels. Describing CNN capacity through a posteriori measures of complexity has
been recently proposed to tackle this apparent paradox. These complexity
measures are usually validated by showing that they correlate empirically with
GE; being empirically larger for networks trained on random vs normal labels.
Focusing on the case of spectral complexity we investigate theoretically and
empirically the insensitivity of the complexity measure to invariances relevant
to CNNs, and show several limitations of spectral complexity that occur as a
result. For a specific formulation of spectral complexity we show that it
results in the same upper bound complexity estimates for convolutional and
locally connected architectures (which don't have the same favorable invariance
properties). This is contrary to common intuition and empirical results.

26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom