Improving Self-Supervised Learning with Contrastive Learning and Masked Modeling: A Comprehensive Study

The article begins by explaining the problem of overfitting in transformer models, which are commonly used for natural language processing tasks such as language translation and text generation. Overfitting occurs when a model becomes too complex and learns the noise in the training data rather than the underlying patterns, resulting in poor generalization performance on unseen data. The author proposes contrastive learning (CL) as a solution to this problem, which involves training a model to predict whether two inputs are similar or dissimilar.
The author then provides an overview of the context, including the average MSE and table 13, which show the performance of different models on various tasks. The author notes that the best performing models have a hierarchical design of complementary CL, which involves creating a hierarchy of positive and negative examples to train the model. The author also includes figure 4, which shows the performance of different models on a particular task, demonstrating the effectiveness of CL in improving performance.
The author then discusses the effectiveness of CL in improving the performance of transformer models, using tables 8, 9, and 11 to demonstrate the impact of CL on different pretraining tasks. The author concludes that CL can significantly improve the performance of transformer models on a variety of natural language processing tasks, and that hierarchical design of complementary CL is particularly effective.
In summary, the article explores the use of contrastive learning in transformer models to improve their performance on various natural language processing tasks. The author provides an overview of the context, including the average MSE and table 13, which show the performance of different models on various tasks. The author then discusses the effectiveness of CL in improving the performance of transformer models, using tables 8, 9, and 11 to demonstrate the impact of CL on different pretraining tasks. The author concludes that CL can significantly improve the performance of transformer models on a variety of natural language processing tasks, and that hierarchical design of complementary CL is particularly effective.