Performed different learning procedures on the STL10 dataset - supervised learning, semi-supervised learning and self-supervised learning
Supervised Learning
Used ResNet-50 Architecture and got validation accuracy of 68.7
Semi-Supervised Learning
Used Pseudo-Labeling method using the same encoder architecture as in supervised learning
Model
Supervised Validation Accuracy
Semi-Supervised Validation Accuracy
Change in Accuracy
CNN Model
59.4
64.62
5.08
ResNet-50 Model
68.73
72
3.27
Self-Supervised Learning
For this I used the SimClr framework for contrastive learning and get a valiation accuracy of 53.30%
AutoAugment
I tried to implement semi-supervised tasks using SimClr and augment images using AutoAugment method. The operations we will be using are shearing, translating, rotation,
auto_contrasting, brightness, sharpness, cutout, etc., and the policies for each augmentation are selected randomly and applied in our dataset for producing image augmentations