Multi-task Learning by Leveraging the Semantic Information

• URL: http://arxiv.org/abs/2103.02546v1
• Date: Wed, 3 Mar 2021 17:36:35 GMT
• Title: Multi-task Learning by Leveraging the Semantic Information
• Authors: Fan Zhou, Brahim Chaib-draa, Boyu Wang
• Abstract summary: We propose to leverage the label information in multi-task learning by exploring the semantic conditional relations among tasks. Our analysis also leads to a concrete algorithm that jointly matches the semantic distribution and controls label distribution divergence.
• Score: 14.397128692867799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One crucial objective of multi-task learning is to align distributions across tasks so that the information between them can be transferred and shared. However, existing approaches only focused on matching the marginal feature distribution while ignoring the semantic information, which may hinder the learning performance. To address this issue, we propose to leverage the label information in multi-task learning by exploring the semantic conditional relations among tasks. We first theoretically analyze the generalization bound of multi-task learning based on the notion of Jensen-Shannon divergence, which provides new insights into the value of label information in multi-task learning. Our analysis also leads to a concrete algorithm that jointly matches the semantic distribution and controls label distribution divergence. To confirm the effectiveness of the proposed method, we first compare the algorithm with several baselines on some benchmarks and then test the algorithms under label space shift conditions. Empirical results demonstrate that the proposed method could outperform most baselines and achieve state-of-the-art performance, particularly showing the benefits under the label shift conditions.

Related papers

• Leveraging knowledge distillation for partial multi-task learning from multiple remote sensing datasets [2.1178416840822023]
  Partial multi-task learning where training examples are annotated for one of the target tasks is a promising idea in remote sensing. This paper proposes using knowledge distillation to replace the need of ground truths for the alternate task and enhance the performance of such approach.
  arXiv  Detail & Related papers  (2024-05-24T09:48:50Z)
• Sharing Knowledge in Multi-Task Deep Reinforcement Learning [57.38874587065694]
  We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We prove this by providing theoretical guarantees that highlight the conditions for which is convenient to share representations among tasks.
  arXiv  Detail & Related papers  (2024-01-17T19:31:21Z)
• 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)
• Multi-Task Consistency for Active Learning [18.794331424921946]
  Inconsistency-based active learning has proven to be effective in selecting informative samples for annotation. We propose a novel multi-task active learning strategy for two coupled vision tasks: object detection and semantic segmentation. Our approach achieves 95% of the fully-trained performance using only 67% of the available data.
  arXiv  Detail & Related papers  (2023-06-21T17:34:31Z)
• Deep Semi-supervised Learning with Double-Contrast of Features and Semantics [2.2230089845369094]
  This paper proposes an end-to-end deep semi-supervised learning double contrast of semantic and feature. We leverage information theory to explain the rationality of double contrast of semantics and features.
  arXiv  Detail & Related papers  (2022-11-28T09:08:19Z)
• Variational Distillation for Multi-View Learning [104.17551354374821]
  We design several variational information bottlenecks to exploit two key characteristics for multi-view representation learning. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels.
  arXiv  Detail & Related papers  (2022-06-20T03:09:46Z)
• Gap Minimization for Knowledge Sharing and Transfer [24.954256258648982]
  In this paper, we introduce the notion of emphperformance gap, an intuitive and novel measure of the distance between learning tasks. We show that the performance gap can be viewed as a data- and algorithm-dependent regularizer, which controls the model complexity and leads to finer guarantees. We instantiate this principle with two algorithms: 1. gapBoost, a novel and principled boosting algorithm that explicitly minimizes the performance gap between source and target domains for transfer learning; and 2. gapMTNN, a representation learning algorithm that reformulates gap minimization as semantic conditional matching
  arXiv  Detail & Related papers  (2022-01-26T23:06:20Z)
• Learning Multiple Dense Prediction Tasks from Partially Annotated Data [41.821234589075445]
  We look at jointly learning of multiple dense prediction tasks on partially annotated data, which we call multi-task partially-supervised learning. We propose a multi-task training procedure that successfully leverages task relations to supervise its multi-task learning when data is partially annotated. We rigorously demonstrate that our proposed method effectively exploits the images with unlabelled tasks and outperforms existing semi-supervised learning approaches and related methods on three standard benchmarks.
  arXiv  Detail & Related papers  (2021-11-29T19:03:12Z)
• 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)
• Concurrent Discrimination and Alignment for Self-Supervised Feature Learning [52.213140525321165]
  Existing self-supervised learning methods learn by means of pretext tasks which are either (1) discriminating that explicitly specify which features should be separated or (2) aligning that precisely indicate which features should be closed together. In this work, we combine the positive aspects of the discriminating and aligning methods, and design a hybrid method that addresses the above issue. Our method explicitly specifies the repulsion and attraction mechanism respectively by discriminative predictive task and concurrently maximizing mutual information between paired views. Our experiments on nine established benchmarks show that the proposed model consistently outperforms the existing state-of-the-art results of self-supervised and transfer
  arXiv  Detail & Related papers  (2021-08-19T09:07:41Z)
• Multitask Learning for Class-Imbalanced Discourse Classification [74.41900374452472]
  We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP.
  arXiv  Detail & Related papers  (2021-01-02T07:13:41Z)

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.