Learning representation from unlabeled time series data is a challenging problem. Most existing self-supervised and unsupervised approaches in the time-series domain do not capture low and high frequency features at the same time. Further some of these methods employ large scale models like transformers or rely on computationally expensive techniques such as contrastive learning. To tackle these problems, we propose a non-contrastive self-supervised learning approach which efficiently captures low and high frequency time varying features in a cost effective manner. Our method takes raw time series data as input and creates two different augmented views for two branches of the model, by randomly sampling the augmentations from same family. Following the terminology of BYOL [1], the two branches are called as online and target network which allow bootstrapping of the latent representation. In contrast to BYOL, where a backbone encoder is followed by multilayer perceptron (MLP) heads, the proposed model contains additional temporal convolutional network (TCN) heads. As the augmented views are passed through large kernel convolution blocks of encoder, the subsequent combination of MLP and TCN enables an effective representation of low as well as high frequency time varying features due to the varying receptive fields. The two modules (MLP and TCN) act in a complementary manner. We train online network where each module learns to predict the outcome of respective module of target network branch. To demonstrate the robustness of our model we performed extensive experiments and ablation studies on five real-world time-series datasets. Our method achieved state-of-art performance on all five real-world datasets.
Point cloud has gained a lot of attention with the availability of a large amount of point cloud data and increasing applications like city planning and self-driving cars. However, current methods, often rely on labeled information and costly processing, such as converting point cloud to voxel. We propose a self-supervised learning approach to tackle these problems, combating labelling and additional memory cost issues. Our proposed method achieves results comparable to supervised and unsupervised baselines on the widely used benchmark datasets for self-supervised point cloud classification like ShapeNet, ModelNet10/40.
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