Basic Algorithm And Framework Research On Contrastive Learning

Contrastive learning aims to train a model that can extract transferable feature representations from unlabeled data,i.e.,a pre-trained model.This paper investigates the basic contrastive learning algorithms and frameworks for learning higher quality and generalizable feature representations.Focusing on two problems: insufficient information about "semantic classes" and insufficient information about instance features,this paper proposes two new contrastive learning methods and frameworks.The effectiveness and advancement of the proposed methods are verified through detailed comparison and ablation experiments.The main contents and contributions of this paper are as follows:(1)To address the problem of insufficient information about "semantic classes",this paper proposes a new contrastive learning method and framework based on the Student tdistribution with the neighbor consistency constraint.The method exploits the long-tail property of the Student t-distribution to reduce the importance of the hard negative samples.After that,a neighbor consistency constraint is proposed to enhance the semantic class consistency between simple samples and their nearest negative samples.This method can effectively reduce the negative influence of hard negative samples,so that the contrastive learning model learns a better concept of "semantic class".(2)To address the problem of insufficient information about instance features,this paper proposes a general feature reconstruction amplifier module for adding feature information.The feature reconstruction amplifier module reconstructs the low-dimensional feature embeddings by Gaussian noise vectors and obtains new high-dimensional feature representations.After that,feature information is added to the contrastive learning model through the additional loss in the feature reconstruction amplifier module.The experimental results show that the feature reconstruction amplifier module can effectively add feature information to improve the performance of the contrastive learning model when the lowdimensional feature embeddings cannot provide sufficient discriminative information for the model.Through studying and improving existing contrastive learning algorithms and frameworks,the two contrastive learning methods and frameworks proposed in this paper effectively enhance the semantic class information and instance feature information in contrastive learning.The classification accuracy and transfer learning capability on the five benchmark datasets outperform or are comparable to state-of-the-art contrastive learning methods.Compared with the baseline method,the proposed method achieves a classification accuracy of 66.72% on the CIFAR-100 dataset with a 4.07% improvement,and an accuracy of 61.68% on the transfer learning task from the STL-10 dataset to the CIFAR-100 dataset with a 4.44% improvement.There are also some parts that need to be improved in the future,such as stabilizing the training process of the model and designing new losses to add specific feature information.The two methods proposed in this paper will be further studied and improved in future work,so as to make more contributions to theoretical research in the field of self-supervised learning and provide higher quality technical support in practical problems such as large-scale data mining and knowledge discovery.