Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Memory Integrity of CNNs for Cross-Dataset Facial Expression Recognition   Facial expression recognition is a major problem in the domain of artificial
intelligence. One of the best ways to solve this problem is the use of
convolutional neural networks (CNNs). However, a large amount of data is
required to train properly these networks but most of the datasets available
for facial expression recognition are relatively small. A common way to
circumvent the lack of data is to use CNNs trained on large datasets of
different domains and fine-tuning the layers of such networks to the target
domain. However, the fine-tuning process does not preserve the memory integrity
as CNNs have the tendency to forget patterns they have learned. In this paper,
we evaluate different strategies of fine-tuning a CNN with the aim of assessing
the memory integrity of such strategies in a cross-dataset scenario. A CNN
pre-trained on a source dataset is used as the baseline and four adaptation
strategies have been evaluated: fine-tuning its fully connected layers;
fine-tuning its last convolutional layer and its fully connected layers;
retraining the CNN on a target dataset; and the fusion of the source and target
datasets and retraining the CNN. Experimental results on four datasets have
shown that the fusion of the source and the target datasets provides the best
trade-off between accuracy and memory integrity.

31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin