  In this work, we have worked towards two major goals. Firstly, we have
investigated the importance of Batch Normalisation (BN) layers in a
non-contrastive representation learning framework called Bootstrap Your Own
Latent (BYOL). We conducted several experiments to conclude that BN layers are
not necessary for representation learning in BYOL. Moreover, BYOL only learns
from the positive pairs of images but ignores other semantically similar images
in the same input batch. For the second goal, we have introduced two new loss
functions to determine the semantically similar pairs in the same input batch
of images and reduce the distance between their representations. These loss
functions are Cross-Cosine Similarity Loss (CCSL) and Cross-Sigmoid Similarity
Loss (CSSL). Using the proposed loss functions, we are able to surpass the
performance of Vanilla BYOL (71.04%) by training the BYOL framework using CCSL
loss (76.87%) on the STL10 dataset. BYOL trained using CSSL loss performs
comparably with Vanilla BYOL.
