Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Residual Continual Learning   We propose a novel continual learning method called Residual Continual
Learning (ResCL). Our method can prevent the catastrophic forgetting phenomenon
in sequential learning of multiple tasks, without any source task information
except the original network. ResCL reparameterizes network parameters by
linearly combining each layer of the original network and a fine-tuned network;
therefore, the size of the network does not increase at all. To apply the
proposed method to general convolutional neural networks, the effects of batch
normalization layers are also considered. By utilizing residual-learning-like
reparameterization and a special weight decay loss, the trade-off between
source and target performance is effectively controlled. The proposed method
exhibits state-of-the-art performance in various continual learning scenarios.

33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con