Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PredNet and Predictive Coding: A Critical Review   PredNet, a deep predictive coding network developed by Lotter et al.,
combines a biologically inspired architecture based on the propagation of
prediction error with self-supervised representation learning in video. While
the architecture has drawn a lot of attention and various extensions of the
model exist, there is a lack of a critical analysis. We fill in the gap by
evaluating PredNet both as an implementation of the predictive coding theory
and as a self-supervised video prediction model using a challenging video
action classification dataset. We design an extended model to test if
conditioning future frame predictions on the action class of the video improves
the model performance. We show that PredNet does not yet completely follow the
principles of predictive coding. The proposed top-down conditioning leads to a
performance gain on synthetic data, but does not scale up to the more complex
real-world action classification dataset. Our analysis is aimed at guiding
future research on similar architectures based on the predictive coding theory.

23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat