The supervised learning architectures generally require a massive amount of labeled data. Acquiring this vast amount of high-quality labeled data can turn out to be a very costly and time-consuming task. The main idea behind self-supervised methods in deep learning is to learn the patterns from a given set of unlabelled data and fine-tune the model with few labeled data. Self-supervised learning using residual networks has recently progressed, but they still underperform by a large margin corresponding to supervised residual network models on ImageNet classification benchmarks. This poor performance has rendered the use of self-supervised models in performance-critical scenarios till this point.
Jan-23-2022, 04:50:07 GMT