Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement

• URL: http://arxiv.org/abs/2402.03557v2
• Date: Wed, 17 Apr 2024 01:01:17 GMT
• Title: Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement
• Authors: Dayou Mao, Yuhao Chen, Yifan Wu, Maximilian Gilles, Alexander Wong,
• Abstract summary: In this paper, we focus on the aforementioned efficiency aspects of existing MTL methods. We first carry out large-scale experiments of the methods with smaller backbones and on a the MetaGraspNet dataset as a new test ground. We also propose Feature Disentanglement measure as a novel and efficient identifier of the challenges in MTL.
• Score: 69.51496713076253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the main motivations of MTL is to develop neural networks capable of inferring multiple tasks simultaneously. While countless methods have been proposed in the past decade investigating robust model architectures and efficient training algorithms, there is still lack of understanding of these methods when applied on smaller feature extraction backbones, the generalizability of the commonly used fast approximation technique of replacing parameter-level gradients with feature level gradients, and lack of comprehensive understanding of MTL challenges and how one can efficiently and effectively identify the challenges. In this paper, we focus on the aforementioned efficiency aspects of existing MTL methods. We first carry out large-scale experiments of the methods with smaller backbones and on a the MetaGraspNet dataset as a new test ground. We also compare the existing methods with and without using the fast gradient surrogate and empirically study the generalizability of this technique. Lastly, we propose Feature Disentanglement measure as a novel and efficient identifier of the challenges in MTL, and propose Ranking Similarity score as an evaluation metric for different identifiers to prove the faithfulness of our method.

Related papers

• Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling [0.0]
  This study proposes a different approach that integrates gradient-based update through continuous relaxation, combined with Quasi-Quantum Annealing (QQA) Numerical experiments demonstrate that our method is a competitive general-purpose solver, achieving performance comparable to iSCO and learning-based solvers.
  arXiv  Detail & Related papers  (2024-09-02T12:55:27Z)
• Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning [8.493889694402478]
  Key challenge in multi-task learning (MTL) is balancing individual task losses during neural network training to improve performance and efficiency. We propose a novel task-weighting method by building on the most prevalent approach of Uncertainty Weighting. Our approach yields comparable results to the analyticallyly prohibitive, brute-force approach of Scalarization.
  arXiv  Detail & Related papers  (2024-08-15T07:10:17Z)
• Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis [53.38518232934096]
  Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. We propose an MTL approach at the intersection between task clustering and feature transformation based on a two-phase iterative aggregation of targets and features. In both phases, a key aspect is to preserve the interpretability of the reduced targets and features through the aggregation with the mean, which is motivated by applications to Earth science.
  arXiv  Detail & Related papers  (2024-06-12T08:30:16Z)
• When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review [7.776434991976473]
  Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. This review focuses on how MTL could be utilised under different partial supervision settings to address these challenges.
  arXiv  Detail & Related papers  (2023-07-25T20:08:41Z)
• Improving Multi-task Learning via Seeking Task-based Flat Regions [38.28600737969538]
  Multi-Task Learning (MTL) is a powerful learning paradigm for training deep neural networks that allows learning more than one objective by a single backbone. There is an emerging line of work in MTL that focuses on manipulating the task gradient to derive an ultimate gradient descent direction. We propose to leverage a recently introduced training method, named Sharpness-aware Minimization, which can enhance model generalization ability on single-task learning.
  arXiv  Detail & Related papers  (2022-11-24T17:19:30Z)
• An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification [97.28167655721766]
  We propose a novel doubly accelerated gradient descent (ADSGD) method for sparsity regularized loss minimization problems. We first prove that ADSGD can achieve a linear convergence rate and lower overall computational complexity.
  arXiv  Detail & Related papers  (2022-08-11T22:27:22Z)
• Adaptive neighborhood Metric learning [184.95321334661898]
  We propose a novel distance metric learning algorithm, named adaptive neighborhood metric learning (ANML) ANML can be used to learn both the linear and deep embeddings. The emphlog-exp mean function proposed in our method gives a new perspective to review the deep metric learning methods.
  arXiv  Detail & Related papers  (2022-01-20T17:26:37Z)
• MAML is a Noisy Contrastive Learner [72.04430033118426]
  Model-agnostic meta-learning (MAML) is one of the most popular and widely-adopted meta-learning algorithms nowadays. We provide a new perspective to the working mechanism of MAML and discover that: MAML is analogous to a meta-learner using a supervised contrastive objective function. We propose a simple but effective technique, zeroing trick, to alleviate such interference.
  arXiv  Detail & Related papers  (2021-06-29T12:52:26Z)
• Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation [19.792537914018933]
  Multi-task learning (MTL) aims to improve the generalization of several related tasks by learning them jointly. Modern meta-learning allows unseen tasks with limited labels during the test phase, in the hope of fast adaptation over them. We show that MTL shares the same optimization formulation with a class of gradient-based meta-learning (GBML) algorithms.
  arXiv  Detail & Related papers  (2021-06-16T17:58:23Z)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)

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.