Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CNLL: A Semi-supervised Approach For Continual Noisy Label Learning   The task of continual learning requires careful design of algorithms that can
tackle catastrophic forgetting. However, the noisy label, which is inevitable
in a real-world scenario, seems to exacerbate the situation. While very few
studies have addressed the issue of continual learning under noisy labels, long
training time and complicated training schemes limit their applications in most
cases. In contrast, we propose a simple purification technique to effectively
cleanse the online data stream that is both cost-effective and more accurate.
After purification, we perform fine-tuning in a semi-supervised fashion that
ensures the participation of all available samples. Training in this fashion
helps us learn a better representation that results in state-of-the-art (SOTA)
performance. Through extensive experimentation on 3 benchmark datasets, MNIST,
CIFAR10 and CIFAR100, we show the effectiveness of our proposed approach. We
achieve a 24.8% performance gain for CIFAR10 with 20% noise over previous SOTA
methods. Our code is publicly available.

7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
19
The categories of this paper are: eess