  Recent breakthroughs in the field of semi-supervised learning have achieved
results that match state-of-the-art traditional supervised learning methods.
Most successful semi-supervised learning approaches in computer vision focus on
leveraging huge amount of unlabeled data, learning the general representation
via data augmentation and transformation, creating pseudo labels, implementing
different loss functions, and eventually transferring this knowledge to more
task-specific smaller models. In this paper, we aim to conduct our analyses on
three different aspects of SimCLR, the current state-of-the-art semi-supervised
learning framework for computer vision. First, we analyze properties of
contrast learning on fine-tuning, as we understand that contrast learning is
what makes this method so successful. Second, we research knowledge
distillation through teacher-forcing paradigm. We observe that when the teacher
and the student share the same base model, knowledge distillation will achieve
better result. Finally, we study how transfer learning works and its
relationship with the number of classes on different data sets. Our results
indicate that transfer learning performs better when number of classes are
smaller.
