COCOA: Cross Modality Contrastive Learning for Sensor Data
- URL: http://arxiv.org/abs/2208.00467v2
- Date: Wed, 3 Aug 2022 22:52:59 GMT
- Title: COCOA: Cross Modality Contrastive Learning for Sensor Data
- Authors: Shohreh Deldari, Hao Xue, Aaqib Saeed, Daniel V. Smith, Flora D. Salim
- Abstract summary: COCOA (Cross mOdality COntrastive leArning) is a self-supervised model that employs a novel objective function to learn quality representations from multisensor data.
We show that COCOA achieves superior classification performance to all other approaches.
- Score: 9.440900386313213
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Self-Supervised Learning (SSL) is a new paradigm for learning discriminative
representations without labelled data and has reached comparable or even
state-of-the-art results in comparison to supervised counterparts. Contrastive
Learning (CL) is one of the most well-known approaches in SSL that attempts to
learn general, informative representations of data. CL methods have been mostly
developed for applications in computer vision and natural language processing
where only a single sensor modality is used. A majority of pervasive computing
applications, however, exploit data from a range of different sensor
modalities. While existing CL methods are limited to learning from one or two
data sources, we propose COCOA (Cross mOdality COntrastive leArning), a
self-supervised model that employs a novel objective function to learn quality
representations from multisensor data by computing the cross-correlation
between different data modalities and minimizing the similarity between
irrelevant instances. We evaluate the effectiveness of COCOA against eight
recently introduced state-of-the-art self-supervised models, and two supervised
baselines across five public datasets. We show that COCOA achieves superior
classification performance to all other approaches. Also, COCOA is far more
label-efficient than the other baselines including the fully supervised model
using only one-tenth of available labelled data.
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