Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning

• URL: http://arxiv.org/abs/2110.14202v1
• Date: Wed, 27 Oct 2021 06:23:45 GMT
• Title: Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning
• Authors: Milad Abdollahzadeh, Touba Malekzadeh, Ngai-Man Cheung
• Abstract summary: Multimodal meta-learning is a recent problem that extends conventional few-shot meta-learning by generalizing its setup to diverse multimodal task distributions. Previous work claims that a single meta-learner trained on a multimodal distribution can sometimes outperform multiple specialized meta-learners trained on individual unimodal distributions. Our work makes two contributions to multimodal meta-learning. First, we propose a method to quantify knowledge transfer between tasks of different modes at a micro-level. Second, inspired by hard parameter sharing in multi-task learning and a new interpretation of related work, we propose a new multimodal meta-learn
• Score: 33.19179706038397
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multimodal meta-learning is a recent problem that extends conventional few-shot meta-learning by generalizing its setup to diverse multimodal task distributions. This setup makes a step towards mimicking how humans make use of a diverse set of prior skills to learn new skills. Previous work has achieved encouraging performance. In particular, in spite of the diversity of the multimodal tasks, previous work claims that a single meta-learner trained on a multimodal distribution can sometimes outperform multiple specialized meta-learners trained on individual unimodal distributions. The improvement is attributed to knowledge transfer between different modes of task distributions. However, there is no deep investigation to verify and understand the knowledge transfer between multimodal tasks. Our work makes two contributions to multimodal meta-learning. First, we propose a method to quantify knowledge transfer between tasks of different modes at a micro-level. Our quantitative, task-level analysis is inspired by the recent transference idea from multi-task learning. Second, inspired by hard parameter sharing in multi-task learning and a new interpretation of related work, we propose a new multimodal meta-learner that outperforms existing work by considerable margins. While the major focus is on multimodal meta-learning, our work also attempts to shed light on task interaction in conventional meta-learning. The code for this project is available at https://miladabd.github.io/KML.

Related papers

• MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks [63.016244188951696]
  We propose a method for few-shot learning with fewer tasks, which is by metaulation. We modify parameters at various batch levels to increase the meta-training tasks. We also introduce learning variational feature hierarchies by incorporating the variationalulation.
  arXiv  Detail & Related papers  (2023-05-17T15:47:47Z)
• 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)
• Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners [67.5865966762559]
  We study whether sparsely activated Mixture-of-Experts (MoE) improve multi-task learning. We devise task-aware gating functions to route examples from different tasks to specialized experts. This results in a sparsely activated multi-task model with a large number of parameters, but with the same computational cost as that of a dense model.
  arXiv  Detail & Related papers  (2022-04-16T00:56:12Z)
• 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)
• The Effect of Diversity in Meta-Learning [79.56118674435844]
  Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that task distribution plays a vital role in the model's performance. We study different task distributions on a myriad of models and datasets to evaluate the effect of task diversity on meta-learning algorithms.
  arXiv  Detail & Related papers  (2022-01-27T19:39:07Z)
• Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction [125.18248926508045]
  We propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for both multimodal fusion and multitask learning. CEN dynamically exchanges channels betweenworks of different modalities. For the application of dense image prediction, the validity of CEN is tested by four different scenarios.
  arXiv  Detail & Related papers  (2021-12-04T05:47:54Z)
• Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks [9.278739724750343]
  Multi-task learning refers to a paradigm in machine learning in which a network is trained on various related tasks to facilitate the acquisition of tasks. multitasking is used to indicate, especially in the cognitive science literature, the ability to execute multiple tasks simultaneously. We show that the same tension arises in deep networks and discuss a meta-learning algorithm for an agent to manage this trade-off in an unfamiliar environment.
  arXiv  Detail & Related papers  (2020-07-20T23:26:16Z)
• MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning [82.62433731378455]
  We show that tasks with high affinity at a certain scale are not guaranteed to retain this behaviour at other scales. We propose a novel architecture, namely MTI-Net, that builds upon this finding.
  arXiv  Detail & Related papers  (2020-01-19T21:02:36Z)

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.