With the recent advancements of deep learning-based methods in image classification,the requirement of a huge amount of training data is inevitable to avoid overfitting problems.Moreover,supervised deep learning model...With the recent advancements of deep learning-based methods in image classification,the requirement of a huge amount of training data is inevitable to avoid overfitting problems.Moreover,supervised deep learning models require labelled datasets for train-ing.Preparing such a huge amount of labelled data requires considerable human effort and time.In this scenario,self-supervised models are becoming popular because of their ability to learn even from unlabelled datasets.However,the efficient transfer of knowledge learned by self-supervised models into a target task,is an unsolved problem.This paper proposes a method for the efficient transfer of know-ledge learned by a self-supervised model,into a target task.Hyperparameters such as the number of layers,the number of units in each layer,learning rate,and dropout are automatically tuned in these fully connected(FC)layers using a Bayesian optimization technique called the tree-structured parzen estimator(TPE)approach algorithm.To evaluate the performance of the proposed method,state-of-the-art self-supervised models such as SimClr and SWAV are used to extract the learned features.Experiments are carried out on the CIFAR-10,CIFAR-100,and Tiny ImageNet datasets.The proposed method outperforms the baseline approach with margins of 2.97%,2.45%,and 0.91%for the CIFAR-100,Tiny ImageNet,and CIFAR-10 datasets,respectively.展开更多
文摘With the recent advancements of deep learning-based methods in image classification,the requirement of a huge amount of training data is inevitable to avoid overfitting problems.Moreover,supervised deep learning models require labelled datasets for train-ing.Preparing such a huge amount of labelled data requires considerable human effort and time.In this scenario,self-supervised models are becoming popular because of their ability to learn even from unlabelled datasets.However,the efficient transfer of knowledge learned by self-supervised models into a target task,is an unsolved problem.This paper proposes a method for the efficient transfer of know-ledge learned by a self-supervised model,into a target task.Hyperparameters such as the number of layers,the number of units in each layer,learning rate,and dropout are automatically tuned in these fully connected(FC)layers using a Bayesian optimization technique called the tree-structured parzen estimator(TPE)approach algorithm.To evaluate the performance of the proposed method,state-of-the-art self-supervised models such as SimClr and SWAV are used to extract the learned features.Experiments are carried out on the CIFAR-10,CIFAR-100,and Tiny ImageNet datasets.The proposed method outperforms the baseline approach with margins of 2.97%,2.45%,and 0.91%for the CIFAR-100,Tiny ImageNet,and CIFAR-10 datasets,respectively.
