Meta-Learning for Few-Shot Time Series Classification

Narwariya, Jyoti; Malhotra, Pankaj; Vig, Lovekesh; Shroff, Gautam; Vishnu, T

doi:10.1145/3371158.3371162

Cited by 35 publications

(19 citation statements)

References 31 publications

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“…At present, Meta-learning is widely active in image classification [16,[28][29], image recognition [17,30], object detection [18,[31][32], and text classification [19] and other computer vision and natural language processing fields. And it has gradually drawn much attention in speech recognition [20], audio event recognition [21,33], text-to-speech [22], speaker recognition [23] and other areas of speech signal processing.…”

Section: A Related Work In Meta-learningmentioning

confidence: 99%

Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement

Zhou

2021

IEEE Access

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Separating target speech from noisy signal is important for many realistic applications. Recently, deep neural network (DNN) has been widely used in speech enhancement (SE) and obtained prominent performance improvements. However, the current deep models require a large amount of training data to obtain a good performance. It is still challenging to construct an effective deep speech enhancement model with actual few training samples. At present, meta-learning has become the research focus of few-shot learning due to its capability of quickly process new tasks with few samples by the prior meta-knowledge, but there are very few works applying meta-learning on few-shot speech enhancement. In this paper, we propose a generic meta-learning framework Meta-SE which applies the U-Net as the metalearner, to tackle the few-shot speech enhancement problem. Meta-SE is trained and optimized with the changed speech enhancement tasks to obtain meta-knowledge, and towards better capability of fast and good generalizing to the new unseen noises with few training samples. The experiment results show that the proposed method not only outperforms the state-of-the-arts DNN-SE models under the few-shot conditions, but also learns a more general and flexible model for task adaption. INDEX TERMS Speech enhancement, single-channel, meta-learning, few-shot learning This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: A Related Work In Meta-learningmentioning

confidence: 99%

Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement

Zhou

2021

IEEE Access

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“…Most existing few-shot learning methods were designed for supervised learning [50,4,45,2,54,51,3,16,38,33,17,48,57,18,31,21,9,47,53,43,55,35,26]. Some unsupervised few-shot learning methods have been proposed, such as clustering [24,32] and density estimation [14,46], but they are not for topic modeling.…”

Section: Related Workmentioning

confidence: 99%

Few-shot Learning for Topic Modeling

Iwata¹

2021

Preprint

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Topic models have been successfully used for analyzing text documents. However, with existing topic models, many documents are required for training. In this paper, we propose a neural networkbased few-shot learning method that can learn a topic model from just a few documents. The neural networks in our model take a small number of documents as inputs, and output topic model priors. The proposed method trains the neural networks such that the expected test likelihood is improved when topic model parameters are estimated by maximizing the posterior probability using the priors based on the EM algorithm. Since each step in the EM algorithm is differentiable, the proposed method can backpropagate the loss through the EM algorithm to train the neural networks. The expected test likelihood is maximized by a stochastic gradient descent method using a set of multiple text corpora with an episodic training framework. In our experiments, we demonstrate that the proposed method achieves better perplexity than existing methods using three real-world text document sets.

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“…Although they considered the one-shot learning problem, this method did not provide an end-to end learning framework. [24] recently proposed a deep neural network with triplet loss to address the classification of various time series data including ECG recordings. This method demonstrates the possibility that few-shot learning can work effectively for signal analysis problems; however, they only learn feature embedding net-works so that embedded features can be categorized by a fixed nearest neighborhood classifier.…”

Section: Related Workmentioning

confidence: 99%

Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification

Kim

Chikontwe

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Brain-Computer Interfaces (BCI) based on Electroencephalography (EEG) signals, in particular motor imagery (MI) data have received a lot of attention and show the potential towards the design of key technologies both in healthcare and other industries. MI data is generated when a subject imagines movement of limbs and can be used to aid rehabilitation as well as in autonomous driving scenarios. Thus, classification of MI signals is vital for EEG-based BCI systems. Recently, MI EEG classification techniques using deep learning have shown improved performance over conventional techniques. However, due to inter-subject variability, the scarcity of unseen subject data, and low signal-to-noise ratio, extracting robust features and improving accuracy is still challenging. In this context, we propose a novel two-way few shot network that is able to efficiently learn how to learn representative features of unseen subject categories and how to classify them with limited MI EEG data. The pipeline includes an embedding module that learns feature representations from a set of samples, an attention mechanism for key signal feature discovery, and a relation module for final classification based on relation scores between a support set and a query signal. In addition to the unified learning of feature similarity and a few shot classifier, our method leads to emphasize informative features in support data relevant to the query data, which generalizes better on unseen subjects. For evaluation, we used the BCI competition IV 2b dataset and achieved an 9.3% accuracy improvement in the 20-shot classification task with state-of-the-art performance. Experimental results demonstrate the effectiveness of employing attention and the overall generality of our method.

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Meta-Learning for Few-Shot Time Series Classification

Cited by 35 publications

References 31 publications

Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement

Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement

Few-shot Learning for Topic Modeling

Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification

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