Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning   Lifelong learning has attracted much attention, but existing works still
struggle to fight catastrophic forgetting and accumulate knowledge over long
stretches of incremental learning. In this work, we propose PODNet, a model
inspired by representation learning. By carefully balancing the compromise
between remembering the old classes and learning new ones, PODNet fights
catastrophic forgetting, even over very long runs of small incremental tasks
--a setting so far unexplored by current works. PODNet innovates on existing
art with an efficient spatial-based distillation-loss applied throughout the
model and a representation comprising multiple proxy vectors for each class. We
validate those innovations thoroughly, comparing PODNet with three
state-of-the-art models on three datasets: CIFAR100, ImageNet100, and
ImageNet1000. Our results showcase a significant advantage of PODNet over
existing art, with accuracy gains of 12.10, 6.51, and 2.85 percentage points,
respectively. Code is available at
https://github.com/arthurdouillard/incremental_learning.pytorch

9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an