Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Continual Learning with Recursive Gradient Optimization   Learning multiple tasks sequentially without forgetting previous knowledge,
called Continual Learning(CL), remains a long-standing challenge for neural
networks. Most existing methods rely on additional network capacity or data
replay. In contrast, we introduce a novel approach which we refer to as
Recursive Gradient Optimization(RGO). RGO is composed of an iteratively updated
optimizer that modifies the gradient to minimize forgetting without data replay
and a virtual Feature Encoding Layer(FEL) that represents different long-term
structures with only task descriptors. Experiments demonstrate that RGO has
significantly better performance on popular continual classification benchmarks
when compared to the baselines and achieves new state-of-the-art performance on
20-split-CIFAR100(82.22%) and 20-split-miniImageNet(72.63%). With higher
average accuracy than Single-Task Learning(STL), this method is flexible and
reliable to provide continual learning capabilities for learning models that
rely on gradient descent.

37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph