  Recent research in self-supervised learning (SSL) has shown its capability in
learning useful semantic representations from images for classification tasks.
Through our work, we study the usefulness of SSL for Fine-Grained Visual
Categorization (FGVC). FGVC aims to distinguish objects of visually similar sub
categories within a general category. The small inter-class, but large
intra-class variations within the dataset makes it a challenging task. The
limited availability of annotated labels for such a fine-grained data
encourages the need for SSL, where additional supervision can boost learning
without the cost of extra annotations. Our baseline achieves $86.36\%$ top-1
classification accuracy on CUB-200-2011 dataset by utilizing random crop
augmentation during training and center crop augmentation during testing. In
this work, we explore the usefulness of various pretext tasks, specifically,
rotation, pretext invariant representation learning (PIRL), and deconstruction
and construction learning (DCL) for FGVC. Rotation as an auxiliary task
promotes the model to learn global features, and diverts it from focusing on
the subtle details. PIRL that uses jigsaw patches attempts to focus on
discriminative local regions, but struggles to accurately localize them. DCL
helps in learning local discriminating features and outperforms the baseline by
achieving $87.41\%$ top-1 accuracy. The deconstruction learning forces the
model to focus on local object parts, while reconstruction learning helps in
learning the correlation between the parts. We perform extensive experiments to
reason our findings. Our code is available at
https://github.com/mmaaz60/ssl_for_fgvc.
