ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework

• URL: http://arxiv.org/abs/2105.08643v1
• Date: Tue, 18 May 2021 16:15:32 GMT
• Title: ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework
• Authors: Zekai Chen, Maiwang Shi, Xiao Zhang, Haochao Ying
• Abstract summary: Human activity recognition (HAR) in the Internet of Things can be formalized as a multi-task multi-view learning problem. We introduce a novel framework ASM2TV for semi-supervised multi-task multi-view learning.
• Score: 7.64589466094347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many real-world scenarios, such as human activity recognition (HAR) in IoT, can be formalized as a multi-task multi-view learning problem. Each specific task consists of multiple shared feature views collected from multiple sources, either homogeneous or heterogeneous. Common among recent approaches is to employ a typical hard/soft sharing strategy at the initial phase separately for each view across tasks to uncover common knowledge, underlying the assumption that all views are conditionally independent. On the one hand, multiple views across tasks possibly relate to each other under practical situations. On the other hand, supervised methods might be insufficient when labeled data is scarce. To tackle these challenges, we introduce a novel framework ASM2TV for semi-supervised multi-task multi-view learning. We present a new perspective named gating control policy, a learnable task-view-interacted sharing policy that adaptively selects the most desirable candidate shared block for any view across any task, which uncovers more fine-grained task-view-interacted relatedness and improves inference efficiency. Significantly, our proposed gathering consistency adaption procedure takes full advantage of large amounts of unlabeled fragmented time-series, making it a general framework that accommodates a wide range of applications. Experiments on two diverse real-world HAR benchmark datasets collected from various subjects and sources demonstrate our framework's superiority over other state-of-the-arts.

Related papers

• Joint-Task Regularization for Partially Labeled Multi-Task Learning [30.823282043129552]
  Multi-task learning has become increasingly popular in the machine learning field, but its practicality is hindered by the need for large, labeled datasets. We propose Joint-Task Regularization (JTR), an intuitive technique which leverages cross-task relations to simultaneously regularize all tasks in a single joint-task latent space.
  arXiv  Detail & Related papers  (2024-04-02T14:16:59Z)
• Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond [62.406687088097605]
  Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space. We show that MTL can be successful with classification tasks with little, or non-overlapping annotations. We propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching.
  arXiv  Detail & Related papers  (2024-01-02T14:18:11Z)
• Factorized Contrastive Learning: Going Beyond Multi-view Redundancy [116.25342513407173]
  This paper proposes FactorCL, a new multimodal representation learning method to go beyond multi-view redundancy. On large-scale real-world datasets, FactorCL captures both shared and unique information and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2023-06-08T15:17:04Z)
• Multi-Task Learning for Visual Scene Understanding [7.191593674138455]
  This thesis is concerned with multi-task learning in the context of computer vision. We propose several methods that tackle important aspects of multi-task learning. The results show several advances in the state-of-the-art of multi-task learning.
  arXiv  Detail & Related papers  (2022-03-28T16:57:58Z)
• Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division [60.232542918414985]
  Multi-task learning often suffers from negative transfer, sharing information that should be task-specific. This is done by using proto-policies as modules to divide the tasks into simple sub-behaviours that can be shared. We also demonstrate its ability to autonomously divide the tasks into both shared and task-specific sub-behaviours.
  arXiv  Detail & Related papers  (2022-03-28T15:53:17Z)
• On Steering Multi-Annotations per Sample for Multi-Task Learning [79.98259057711044]
  The study of multi-task learning has drawn great attention from the community. Despite the remarkable progress, the challenge of optimally learning different tasks simultaneously remains to be explored. Previous works attempt to modify the gradients from different tasks. Yet these methods give a subjective assumption of the relationship between tasks, and the modified gradient may be less accurate. In this paper, we introduce Task Allocation(STA), a mechanism that addresses this issue by a task allocation approach, in which each sample is randomly allocated a subset of tasks. For further progress, we propose Interleaved Task Allocation(ISTA) to iteratively allocate all
  arXiv  Detail & Related papers  (2022-03-06T11:57:18Z)
• Multi-View representation learning in Multi-Task Scene [4.509968166110557]
  We propose a novel semi-supervised algorithm, termed as Multi-Task Multi-View learning based on Common and Special Features (MTMVCSF) An anti-noise multi-task multi-view algorithm called AN-MTMVCSF is proposed, which has a strong adaptability to noise labels. The effectiveness of these algorithms is proved by a series of well-designed experiments on both real world and synthetic data.
  arXiv  Detail & Related papers  (2022-01-15T11:26:28Z)
• Variational Multi-Task Learning with Gumbel-Softmax Priors [105.22406384964144]
  Multi-task learning aims to explore task relatedness to improve individual tasks. We propose variational multi-task learning (VMTL), a general probabilistic inference framework for learning multiple related tasks.
  arXiv  Detail & Related papers  (2021-11-09T18:49:45Z)
• Semi-supervised Multi-task Learning for Semantics and Depth [88.77716991603252]
  Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance. We propose the Semi-supervised Multi-Task Learning (MTL) method to leverage the available supervisory signals from different datasets. We present a domain-aware discriminator structure with various alignment formulations to mitigate the domain discrepancy issue among datasets.
  arXiv  Detail & Related papers  (2021-10-14T07:43:39Z)
• Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations [7.51557557629519]
  A successful multiview policy could be deployed on a mobile manipulation platform. We demonstrate that a multiview policy can be found through imitation learning by collecting data from a variety of viewpoints. We show that learning from multiview data has little, if any, penalty to performance for a fixed-view task compared to learning with an equivalent amount of fixed-view data.
  arXiv  Detail & Related papers  (2021-04-28T17:43:29Z)
• Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation [11.91574721055601]
  This paper proposes a novel multi-view learning method, named Multi-view Low-rank Preserving Embedding (MvLPE) It integrates different views into one centroid view by minimizing the disagreement term, based on distance or similarity matrix among instances. Experiments on six benchmark datasets demonstrate that the proposed method outperforms its counterparts.
  arXiv  Detail & Related papers  (2020-06-14T12:47:25Z)

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.