Self-paced deep clustering with learning loss

Abstract

Deep clustering has become a popular technique in various fields due to its superior performance over conventional clustering. However, it can be challenging to classify data located near the decision boundary, and difficult or noisy samples can confuse or mislead the training process of deep neural networks, ultimately impacting clustering performance. To address this issue, we propose a novel self-paced deep clustering method that gradually increases the number of samples input into the network from easy to difficult. Our approach involves attaching a loss prediction module to the convolutional neural network to judge the difficulty of samples. The module is robust as it relies on the input contents, not statistical estimates of uncertainty from outputs. Moreover, this module selects the most informative data for the training model, thus enabling the network to converge stably and reach a good optimal solution. Finally, we consider the reconstruction error, clustering loss, and loss prediction error to construct the loss of the model. Experimental results on four image datasets show that our method outperforms state-of-the-art works. The main code can be found at https://github.com/clustering106/SDCLL.