Task Uncertainty Loss Reduce Negative Transfer in Asymmetric Multi-task Feature Learning

• URL: http://arxiv.org/abs/2012.09575v1
• Date: Thu, 17 Dec 2020 13:30:45 GMT
• Title: Task Uncertainty Loss Reduce Negative Transfer in Asymmetric Multi-task Feature Learning
• Authors: Rafael Peres da Silva, Chayaporn Suphavilai, Niranjan Nagarajan
• Abstract summary: Multi-task learning (MTL) can improve task performance overall relative to single-task learning (STL), but can hide negative transfer (NT) Asymmetric multitask feature learning (AMTFL) is an approach that tries to address this by allowing tasks with higher loss values to have smaller influence on feature representations for learning other tasks. We present examples of NT in two datasets (image recognition and pharmacogenomics) and tackle this challenge by using aleatoric homoscedastic uncertainty to capture the relative confidence between tasks, and set weights for task loss.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-task learning (MTL) is frequently used in settings where a target task has to be learnt based on limited training data, but knowledge can be leveraged from related auxiliary tasks. While MTL can improve task performance overall relative to single-task learning (STL), these improvements can hide negative transfer (NT), where STL may deliver better performance for many individual tasks. Asymmetric multitask feature learning (AMTFL) is an approach that tries to address this by allowing tasks with higher loss values to have smaller influence on feature representations for learning other tasks. Task loss values do not necessarily indicate reliability of models for a specific task. We present examples of NT in two orthogonal datasets (image recognition and pharmacogenomics) and tackle this challenge by using aleatoric homoscedastic uncertainty to capture the relative confidence between tasks, and set weights for task loss. Our results show that this approach reduces NT providing a new approach to enable robust MTL.

Related papers

• 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)
• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives [19.90788777476128]
  Multi-task learning (MTL) seeks to learn a single model to accomplish multiple tasks by leveraging shared information among the tasks. Existing MTL models have been known to suffer from negative interference among tasks. We propose ETR-NLP to mitigate task interference through a synergistic combination of non-learnable primitives and explicit task routing.
  arXiv  Detail & Related papers  (2023-08-03T22:34:16Z)
• Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks [0.0]
  Multi-task learning (MTL) can improve the generalization performance of neural networks by sharing representations with related tasks. MTL can also degrade performance through harmful interference between tasks. We propose SLGrad, a sample-level weighting algorithm for multi-task learning with auxiliary tasks.
  arXiv  Detail & Related papers  (2023-06-07T15:29:46Z)
• ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning [59.08197876733052]
  Auxiliary-Task Learning (ATL) aims to improve the performance of the target task by leveraging the knowledge obtained from related tasks. Sometimes, learning multiple tasks simultaneously results in lower accuracy than learning only the target task, known as negative transfer. ForkMerge is a novel approach that periodically forks the model into multiple branches, automatically searches the varying task weights.
  arXiv  Detail & Related papers  (2023-01-30T02:27:02Z)
• "It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning [74.14961250042629]
  Multi-Task Learning (MTL) promises attractive, characterizing the conditions of its success is still an open problem in Deep Learning. Estimateing task affinity for joint learning is a key endeavor. Recent work suggests that the training conditions themselves have a significant impact on the outcomes of MTL. Yet, the literature is lacking a benchmark to assess the effectiveness of tasks affinity estimation techniques.
  arXiv  Detail & Related papers  (2023-01-07T15:16:35Z)
• TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding [0.0]
  We show that a state-of-the-art data augmentation method worsens this problem of overfitting when the task diversity is low. We propose a simple method, TaskMix, which synthesizes new tasks by linearly interpolating existing tasks. We show that TaskMix outperforms baselines, alleviates overfitting when task diversity is low, and does not degrade performance even when it is high.
  arXiv  Detail & Related papers  (2022-09-26T00:37:40Z)
• 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)
• HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks [4.095907708855597]
  Multi-task learning (MTL) can improve performance on a task by sharing representations with one or more related auxiliary-tasks. Usually, MTL-networks are trained on a composite loss function formed by a constant weighted combination of the separate task losses. In practice, constant loss weights lead to poor results for two reasons: (i) for mini-batch based optimisation, the optimal task weights vary significantly from one update to the next depending on mini-batch sample composition. We introduce HydaLearn, an intelligent weighting algorithm that connects main-task gain to the individual task gradients, in order to inform
  arXiv  Detail & Related papers  (2020-08-26T16:04:02Z)
• Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference [75.95287293847697]
  Two common challenges in developing multi-task models are often overlooked in literature. First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning) Second, eliminating adverse interactions amongst tasks, which has been shown to significantly degrade the single-task performance in a multi-task setup (task interference)
  arXiv  Detail & Related papers  (2020-07-24T14:44:46Z)
• Knowledge Distillation for Multi-task Learning [38.20005345733544]
  Multi-task learning (MTL) is to learn one single model that performs multiple tasks for achieving good performance on all tasks and lower cost on computation. Learning such a model requires to jointly optimize losses of a set of tasks with different difficulty levels, magnitudes, and characteristics. We propose a knowledge distillation based method in this work to address the imbalance problem in multi-task learning.
  arXiv  Detail & Related papers  (2020-07-14T08:02:42Z)

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.