Mixture of Self-Supervised Learning
Aristo Renaldo Ruslim Novanto Yudistira Budi Darma Setiawan
Self-supervised learning is popular method because of its ability to learn
features in images without using its labels and is able to overcome limited
labeled datasets used in supervised learning. Self-supervised learning works by
using a pretext task which will be trained on the model before being applied to
a specific task. There are some examples of pretext tasks used in
self-supervised learning in the field of image recognition, namely rotation
prediction, solving jigsaw puzzles, and predicting relative positions on image.
Previous studies have only used one type of transformation as a pretext task.
This raises the question of how it affects if more than one pretext task is
used and to use a gating network to combine all pretext tasks. Therefore, we
propose the Gated Self-Supervised Learning method to improve image
classification which use more than one transformation as pretext task and uses
the Mixture of Expert architecture as a gating network in combining each
pretext task so that the model automatically can study and focus more on the
most useful augmentations for classification. We test performance of the
proposed method in several scenarios, namely CIFAR imbalance dataset
classification, adversarial perturbations, Tiny-Imagenet dataset
classification, and semi-supervised learning. Moreover, there are Grad-CAM and
T-SNE analysis that are used to see the proposed method for identifying
important features that influence image classification and representing data
for each class and separating different classes properly. Our code is in
https://github.com/aristorenaldo/G-SSL