Deep Learning with Little Labelled Data

很少标记数据的深度学习

• 批准号: RGPIN-2019-06706
• 负责人: Gagné, Christian
• 金额: $ 2.99万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739063
• 关键词: Deep; Learning; Little; Labelled; Data

Machine Learning (ML) is a field of Artificial Intelligence (AI) that aims at providing computers with learning abilities, by inferring models from observations and experience. Many of the recent advances of AI have stemmed from ML, in particular the subfield of Deep Learning (DL). DL aims at learning hierarchical structures, in order to extract features from unstructured raw data (e.g., images, sound, text), allowing significant improvements over the state-of-the-art for many well-studied tasks (e.g., object recognition, automatic translation). However, these techniques are data hungry, requiring huge annotated datasets to be able to efficiently learn how to accomplish some specific tasks. Although such datasets are available for some specific well-established tasks, collecting and annotating sufficiently large datasets for a novel application can be expensive, cost- and time-wise. We propose a research program investigating various approaches to improve the efficiency of ML and DL for solving problems where only small annotated datasets available. We are looking to build upon other related or satellite datasets having the same kind of data, but whose annotations, if they exist, are not adapted to the task. From it, we would produce a working model for our current task, by learning (or fine-tuning) on the small datasets with the task-related annotations. The program is organized around four objectives: 1) To investigate how to better learn models by exploiting knowledge extracted from different tasks, which can be reused or adapted to the current context; 2) To improve meta-learning methods that can learn new concepts from only few samples; 3) To develop new approaches to extract better general representations through unsupervised learning; 4) To apply the novel methods to real-world applications, in order to both assess the practicality of the proposed methods and achieve meaningful contributions in the application domain themselves. Methods investigated within this research program have the potential of making ML and DL usable in a variety of contexts where there are no available big datasets well adapted to the specific task at hand. These ML-based systems will assist humans to adapt quickly to a given task by exploiting similar historical tasks; they will have the capacity for a rapid customization to each user. We are aiming at learning better representations of some modalities (e.g., image, text, speech), usable for a variety of purposes. We will look for discovering mechanisms for AI to adapt to new learning tasks, being able to ramp up performance with only a few examples. Finally, we will apply our techniques to domains such as black-box optimization, super-resolution microscopy, and visual place recognition.

机器学习（ML）是人工智能（AI）的一个领域，旨在通过从观察和经验中推断模型来为计算机提供学习能力。人工智能的许多最新进展都源于ML，特别是深度学习（DL）的子领域。DL旨在学习分层结构，以便从非结构化原始数据中提取特征（例如，图像、声音、文本），从而允许对许多已充分研究的任务（例如，对象识别、自动翻译）。然而，这些技术需要大量数据，需要大量带注释的数据集才能有效地学习如何完成一些特定任务。虽然这些数据集可用于某些特定的既定任务，但为新应用程序收集和注释足够大的数据集可能是昂贵的，成本和时间方面的。我们提出了一个研究计划，研究各种方法来提高ML和DL的效率，以解决只有小的注释数据集可用的问题。我们正在寻求建立在其他相关或卫星数据集具有相同类型的数据，但其注释，如果他们存在，是不适合的任务。通过学习（或微调）带有任务相关注释的小数据集，我们将为当前任务生成一个工作模型。该计划围绕四个目标组织：1）研究如何通过利用从不同任务中提取的知识来更好地学习模型，这些知识可以重用或适应当前上下文; 2）改进元学习方法，可以从很少的样本中学习新概念; 3）开发新方法，通过无监督学习来提取更好的一般表示; 4）将新方法应用于实际应用，以评估所提出方法的实用性并在应用领域本身做出有意义的贡献。该研究计划中研究的方法有可能使ML和DL在各种环境中可用，在这些环境中没有适合手头特定任务的可用大数据集。这些基于ML的系统将通过利用类似的历史任务来帮助人类快速适应给定的任务;它们将具有快速定制每个用户的能力。我们的目标是学习一些模态的更好的表示（例如，图像、文本、语音），可用于各种目的。我们将寻找发现AI适应新学习任务的机制，仅用几个例子就能提高性能。最后，我们将把我们的技术应用到黑盒优化、超分辨率显微镜和视觉位置识别等领域。