[6] A Dirty Model for Multi-task Learning. Advances in Neural Information Processing Systems https://papers.nips.cc/paper/4125-a-dirty-model-for-multi-task-learning.pdf [7] Distributed Multi-task Relationship Learning http://arxiv.org/abs/16...
Multitask learning (MTL) is one such widely used technique. In this paper, we seek not only to understand the impact of MTL on worst-group accuracy but also to explore its potential as a tool to address the challenge of group-wise fairness. We primarily consider the standard setting of ...
task network adept at three vital autonomous driving tasks: monocular 3D object detection, semantic segmentation, and dense depth estimation. To counter the challenge of negative transfer, which is the prevalent issue in multi-task learning, we introduce a task-adaptive attention generator. This ...
[5] Taking Advantage of Sparsity in Multi-Task Learninghttp://arxiv.org/pdf/0903.1468 [6] A Dirty Model for Multi-task Learning. Advances in Neural Information Processing Systems https://papers.nips.cc/paper/4125-a-dirty-model-for-multi-task-learning.pdf [7] Distributed Multi-task Relationsh...
Multi-task learning of facial landmarks and expression paper:http://www.uoguelph.ca/~gwtaylor/publications/gwtaylor_crv2014.pdf Multi-Task Deep Visual-Semantic Embedding for Video Thumbnail Selection intro: CVPR 2015 paper:http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Liu_Mult...
by the way,moe和mmoe都属于前面提到的encoder-focused multi task learning architecture分支中的soft parameter sharing。只不过这里并没有施加l2之类的constraint,而是仅仅要求多个相同的expert最终的结果要进行加权求和(如果这也算constraint的话) ok,最后就是mmoe了 ...
What is Multi-Task Learning(MTL)? 例如在自动驾驶中,我们需要实时对图像进行车辆检测、车道线分割、景深估计等 n 个Tasks 。传统的方式使是基于单任务学习(Single-Task Learning,STL),即每个 Task 使用一个独立的模型: 车辆检测道路分割景深估计y1=f车辆检测(image)y2=f道路分割(image)y3=f景深估计(image)...
Awesome Multi-Task Learning A curated list of datasets, codebases, and papers on Multi-Task Learning (MTL), from a Machine Learning perspective. This project greatly appreciates the surveys below, which have been incredibly helpful. We welcome your contributions! If you find any mistakes or omiss...
背景:只专注于单个模型可能会忽略一些相关任务中可能提升目标任务的潜在信息,通过进行一定程度的共享不同任务之间的参数,可能会使原任务泛化更好。广义的讲,只要loss有多个就算MTL,一些别名(joint learning,learning to learn,learning with auxiliary task)
Multi-task learning (MTL) has been widely used in representation learning. However, naively training all tasks simultaneously may lead to the partial training issue, where specific tasks are trained more adequately than others. In this paper, we propose to learn multiple tasks impartially. Specifica...