  Self-Supervised Learning (SSL) has become a prominent approach for acquiring
visual representations across various tasks, yet its application in
fine-grained visual recognition (FGVR) is challenged by the intricate task of
distinguishing subtle differences between categories. To overcome this, we
introduce an novel strategy that boosts SSL's ability to extract critical
discriminative features vital for FGVR. This approach creates synthesized data
pairs to guide the model to focus on discriminative features critical for FGVR
during SSL. We start by identifying non-discriminative features using two main
criteria: features with low variance that fail to effectively separate data and
those deemed less important by Grad-CAM induced from the SSL loss. We then
introduce perturbations to these non-discriminative features while preserving
discriminative ones. A decoder is employed to reconstruct images from both
perturbed and original feature vectors to create data pairs. An encoder is
trained on such generated data pairs to become invariant to variations in
non-discriminative dimensions while focusing on discriminative features,
thereby improving the model's performance in FGVR tasks. We demonstrate the
promising FGVR performance of the proposed approach through extensive
evaluation on a wide variety of datasets.
