Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing
We propose a novel loss weighting algorithm, called loss scale balancing (LSB), for multi-task learning (MTL) of pixelwise vision tasks. An MTL model is trained to estimate multiple pixelwise predictions using an overall loss, which is a linear combination of individual task losses. The proposed algorithm dynamically adjusts the linear weights to learn all tasks effectively. Instead of controlling the trend of each loss value directly, we balance the loss scale --- the product of the loss value and its weight --- periodically. In addition, by evaluating the difficulty of each task based on the previous loss record, the proposed algorithm focuses more on difficult tasks during training. Experimental results show that the proposed algorithm outperforms conventional weighting algorithms for MTL of various pixelwise tasks. Codes are available at https://github.com/jaehanlee-mcl/LSB-MTL.
Code (1)
Tasks
Multi-Task LearningSimilar Papers 제목 키워드 기반
Dual-Balancing for Multi-Task Learning
Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields. However, task balancing problem remains a significant challenge in MTL, with th…
Multi-Task LearningCan we integrate spatial verification methods into neural-network loss functions for atmospheric science?
In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise verification. However, neural networks (NN) …
Multi-label Pixelwise Classification for Reconstruction of Large-scale Urban Areas
Object classification is one of the many holy grails in computer vision and as such has resulted in a very large number of algorithms being proposed already. Specifically in recent years there has been considerable progr…
ClassificationGeneral ClassificationObjectMitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategies
Multi-Task Learning (MTL) is a growing subject of interest in deep learning, due to its ability to train models more efficiently on multiple tasks compared to using a group of conventional single-task models. However, MT…
Multi-Task LearningFully Convolutional Multi-Class Multiple Instance Learning
Multiple instance learning (MIL) can reduce the need for costly annotation in tasks such as semantic segmentation by weakening the required degree of supervision. We propose a novel MIL formulation of multi-class semanti…
Multiple Instance LearningSegmentationSemantic SegmentationWeakly-Supervised Semantic Segmentation