[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...
[5] Taking Advantage of Sparsity in Multi-Task Learningarxiv.org/pdf/0903.1468 [6] A Dirty Model for Multi-task Learning. Advances in Neural Information Processing Systems papers.nips.cc/paper/41 [7] Distributed Multi-task Relationship Learning arxiv.org/abs/1612.0402 [8] Regularized multi-task...
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 ...
多任务学习(multi-task learning,MTL),对于给定的 K≥2 个任务,每个都有其损失函数 Li(θ) ,其中 θ 是所有任务共享的参数集。最终目标是找到最优的 θ 使得所有任务都到达最小的loss,而实际中,我们一般以最小化平均loss为目标: θ∗=argminθ∈Rm{L0(θ)≜1K∑i=1KLi(θ)} 然而,直接优化平均...
多任务学习(multi task learning)简称为MTL。简单来说有多个目标函数loss同时学习的就算多任务学习。多任务既可以每个任务都搞一个模型来学,也可以一个模型多任务学习来一次全搞定的。 作者丨Anticoder@知乎 链接丨https://zhuanlan.zhihu.com/p/59413549
参数的硬共享机制:从几十年前开始到现在这种方式还在流行(Multitask Learning. Autonomous Agents and Multi-Agent Systems[3]),一般认为一个模型中任务越多,通过参数共享降低噪声导致过拟合的风险更低,在参数硬共享机制中loss直接相加就是一种最简单的均值约束。
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...
在Identifying beneficial task relations for multi-task learning in deep neural networks[6]中,作者探究到底是什么让multi-task work, 作者使用严格意义上相同的参数用NLP任务做了对比实验,图中分别是两个任务结合时与单任务loss的对比,大部分多任务的效果比不上单任务,作者的结论是单任务的主要特征在起作用,那些...
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. ...
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 ...