LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning
Not on the map. Why?
Abstract (arXiv)
One of the recent developments in deep learning is generalized zero-shot learning (GZSL), which aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Over the past couple of years, GZSL has picked up traction and several models have been proposed to solve this problem. Whereas an extensive amount of research on GZSL has been carried out in fields such as computer vision and natural language processing, no such research has been carried out to deal with time series data. GZSL is used for applications such as detecting abnormalities from ECG and EEG data and identifying unseen classes from sensor, spectrograph and other devices' data. In this regard, we propose a Latent Embedding for Time Series - GZSL (LETS-GZSL) model that can solve the problem of GZSL for time series classification (TSC). We utilize an embedding-based approach and combine it with attribute vectors to predict the final class labels. We report our results on the widely popular UCR archive datasets. Our framework is able to achieve a harmonic mean value of at least 55% on most of the datasets except when the number of unseen classes is greater than 3 or the amount of data is very low (less than 100 training examples).
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 45 references that are in the map. First posted on arXiv 25 Jul 2022.
Builds on
21 of 45 references.
References · 21
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015Distilling the Knowledge in a Neural Networkcited by 10,076
- 2018Deep learning for time series classification: a reviewcited by 575
- 2021TS2Vec: Towards Universal Representation of Time Seriescited by 463
- 2018The UCR Time Series Archivecited by 421
- 2018The UEA multivariate time series classification archive, 2018cited by 330
- 2018Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoderscited by 206
- 2020A Review of Generalized Zero-Shot Learning Methodscited by 159
- 2016An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wildcited by 146
- 2021HIVE-COTE 2.0: a new meta ensemble for time series classificationcited by 118
- 2021Contrastive Embedding for Generalized Zero-Shot Learningcited by 94
- 2019Leveraging the Invariant Side of Generative Zero-Shot Learningcited by 83
9 more
- 2017Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networkscited by 66
- 2018A review on distance based time series classificationcited by 40
- 2018Preserving Semantic Relations for Zero-Shot Learningcited by 35
- 2017Deep Echo State Network (DeepESN): A Brief Surveycited by 21
- 2018Towards Effective Deep Embedding for Zero-Shot Learningcited by 19
- 2019Insights into LSTM Fully Convolutional Networks for Time Series Classificationcited by 18
- 2019A Meta-Learning Framework for Generalized Zero-Shot Learningcited by 7
- 2022ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modelingcited by 7
- 2018From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Processcited by 5
Built on by
Citing papers per year: 2023: 2.
2 papers.