On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting

• URL: http://arxiv.org/abs/2204.00352v1
• Date: Fri, 1 Apr 2022 10:59:39 GMT
• Title: On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting
• Authors: Wei-Tsung Kao, Yuen-Kwei Wu, Chia Ping Chen, Zhi-Sheng Chen, Yu-Pao Tsai, Hung-Yi Lee
• Abstract summary: User-defined keyword spotting is a task to detect new spoken terms defined by users. Previous works try to incorporate self-supervised learning models or apply meta-learning algorithms. Our result shows that HuBERT combined with Matching network achieves the best result.
• Score: 51.41426141283203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: User-defined keyword spotting is a task to detect new spoken terms defined by users. This can be viewed as a few-shot learning problem since it is unreasonable for users to define their desired keywords by providing many examples. To solve this problem, previous works try to incorporate self-supervised learning models or apply meta-learning algorithms. But it is unclear whether self-supervised learning and meta-learning are complementary and which combination of the two types of approaches is most effective for few-shot keyword discovery. In this work, we systematically study these questions by utilizing various self-supervised learning models and combining them with a wide variety of meta-learning algorithms. Our result shows that HuBERT combined with Matching network achieves the best result and is robust to the changes of few-shot examples.

Related papers

• When Meta-Learning Meets Online and Continual Learning: A Survey [39.53836535326121]
  meta-learning is a data-driven approach to optimize the learning algorithm. Continual learning and online learning, both of which involve incrementally updating a model with streaming data. This paper organizes various problem settings using consistent terminology and formal descriptions.
  arXiv  Detail & Related papers  (2023-11-09T09:49:50Z)
• Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning [38.37682598345653]
  We introduce a multimodal meta-learning approach to bridge the gap between vision and language models. We define a meta-mapper network, acting as a meta-learner, to efficiently bridge frozen large-scale vision and language models. We evaluate our approach on recently proposed multimodal few-shot benchmarks, measuring how rapidly the model can bind novel visual concepts to words.
  arXiv  Detail & Related papers  (2023-02-28T17:46:18Z)
• Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting [77.69172089359606]
  We study multi-modal few-shot object detection (FSOD) in this paper, using both few-shot visual examples and class semantic information for detection. Our approach is motivated by the high-level conceptual similarity of (metric-based) meta-learning and prompt-based learning. We comprehensively evaluate the proposed multi-modal FSOD models on multiple few-shot object detection benchmarks, achieving promising results.
  arXiv  Detail & Related papers  (2022-04-16T16:45:06Z)
• Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning [113.05118113697111]
  Few-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with only a limited amount of labeled data. Research literature on few-shot learning exhibits great diversity, while different algorithms often excel at different few-shot learning scenarios. We present Meta Navigator, a framework that attempts to solve the limitation in few-shot learning by seeking a higher-level strategy.
  arXiv  Detail & Related papers  (2021-09-13T07:20:01Z)
• Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling [29.218780709974244]
  We propose a few-shot learning scheme that learns to bridge metric spaces of intent and slot on data-rich domains. Our model significantly outperforms the strong baselines in one and five shots settings.
  arXiv  Detail & Related papers  (2021-05-25T15:07:11Z)
• Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation [26.296412053816233]
  We propose a meta-learning framework for few-shot word sense disambiguation. The goal is to learn to disambiguate unseen words from only a few labeled instances. We extend several popular meta-learning approaches to this scenario, and analyze their strengths and weaknesses.
  arXiv  Detail & Related papers  (2020-04-29T17:33:31Z)
• Meta-Meta Classification for One-Shot Learning [11.27833234287093]
  We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of learners, where each learner has high bias and low variance. We evaluate the approach on a one-shot, one-class-versus-all classification task and show that it is able to outperform traditional meta-learning as well as ensembling approaches.
  arXiv  Detail & Related papers  (2020-04-17T07:05:03Z)
• Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning [79.25478727351604]
  We explore a simple process: meta-learning over a whole-classification pre-trained model on its evaluation metric. We observe this simple method achieves competitive performance to state-of-the-art methods on standard benchmarks.
  arXiv  Detail & Related papers  (2020-03-09T20:06:36Z)
• Provable Meta-Learning of Linear Representations [114.656572506859]
  We provide fast, sample-efficient algorithms to address the dual challenges of learning a common set of features from multiple, related tasks, and transferring this knowledge to new, unseen tasks. We also provide information-theoretic lower bounds on the sample complexity of learning these linear features.
  arXiv  Detail & Related papers  (2020-02-26T18:21:34Z)
• Revisiting Meta-Learning as Supervised Learning [69.2067288158133]
  We aim to provide a principled, unifying framework by revisiting and strengthening the connection between meta-learning and traditional supervised learning. By treating pairs of task-specific data sets and target models as (feature, label) samples, we can reduce many meta-learning algorithms to instances of supervised learning. This view not only unifies meta-learning into an intuitive and practical framework but also allows us to transfer insights from supervised learning directly to improve meta-learning.
  arXiv  Detail & Related papers  (2020-02-03T06:13:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.