Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pseudo-Rehearsal for Continual Learning with Normalizing Flows   Catastrophic forgetting (CF) happens whenever a neural network overwrites
past knowledge while being trained on new tasks. Common techniques to handle CF
include regularization of the weights (using, e.g., their importance on past
tasks), and rehearsal strategies, where the network is constantly re-trained on
past data. Generative models have also been applied for the latter, in order to
have endless sources of data. In this paper, we propose a novel method that
combines the strengths of regularization and generative-based rehearsal
approaches. Our generative model consists of a normalizing flow (NF), a
probabilistic and invertible neural network, trained on the internal embeddings
of the network. By keeping a single NF conditioned on the task, we show that
our memory overhead remains constant. In addition, exploiting the invertibility
of the NF, we propose a simple approach to regularize the network's embeddings
with respect to past tasks. We show that our method performs favorably with
respect to state-of-the-art approaches in the literature, with bounded
computational power and memory overheads.

21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph