Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Overcoming Catastrophic Forgetting via Direction-Constrained
  Optimization   This paper studies a new design of the optimization algorithm for training
deep learning models with a fixed architecture of the classification network in
a continual learning framework. The training data is non-stationary and the
non-stationarity is imposed by a sequence of distinct tasks. We first analyze a
deep model trained on only one learning task in isolation and identify a region
in network parameter space, where the model performance is close to the
recovered optimum. We provide empirical evidence that this region resembles a
cone that expands along the convergence direction. We study the principal
directions of the trajectory of the optimizer after convergence and show that
traveling along a few top principal directions can quickly bring the parameters
outside the cone but this is not the case for the remaining directions. We
argue that catastrophic forgetting in a continual learning setting can be
alleviated when the parameters are constrained to stay within the intersection
of the plausible cones of individual tasks that were so far encountered during
training. Based on this observation we present our direction-constrained
optimization (DCO) method, where for each task we introduce a linear
autoencoder to approximate its corresponding top forbidden principal
directions. They are then incorporated into the loss function in the form of a
regularization term for the purpose of learning the coming tasks without
forgetting. Furthermore, in order to control the memory growth as the number of
tasks increases, we propose a memory-efficient version of our algorithm called
compressed DCO (DCO-COMP) that allocates a memory of fixed size for storing all
autoencoders. We empirically demonstrate that our algorithm performs favorably
compared to other state-of-art regularization-based continual learning methods.

24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat