In this paper, we propose a Robust Multi- Task Feature Learning algorithm (rMTFL) which simultaneously captures a common set of features among relevant tasks and identifies outlier tasks. Specifically, we decompose the weight (model) matrix for all tasks into two components. We impose the well-...
Ditto是通过减少对全局模型的依赖来进行联邦个性化的。除了Ditto还有其他个性化方法也能达到相似的技术,最后实验部分也有提到,那为啥深入研究Ditto呢?因为效果好,而且简单 Ditto与将朝其平均值正则化个性化模型的工作有很大的关系,类似于经典的均值正则化MTL; 不同之处在于Ditto是正则化全局模型,而不是平均的个性化模型。
Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap 来自 arXiv.org 喜欢 0 阅读量: 133 作者:TH Park,S D'Amico 摘要: This work presents Spacecraft Pose Network v2 (SPNv2), a Convolutional Neural Network (CNN) for pose estimation of noncooperative ...
To handle possible misspecification of the structure, we propose a method named Adaptive and Robust MUlti-Task Learning (ARMUL):min Θ ∈ R d × m , Γ ∈ Ω { ∑ j = 1 m w j [ f j ( θ j ) + λ j ‖ θ j − γ j ‖ 2 ] } ....
aware Network (MPN), which is designed to extract semantically aligned part-level features from pedestrian images. MPN solvesthe body part misalignment problem via multi-task learning (MTL) in the training stage. More specif i cally, it builds one main task (MT)and one auxiliary task (AT) ...
与常见的multi-task RL的设计不同,一般来说是给每一个任务赋予一个one-hot编码,然后为神经网络设计一个task encoder,用来将one-hot编码映射为一个连续空间表征向量e。但是在TD-MPC2中,e是一个神经网络参数,在训练过程中通过梯度下降和其他所有模型一同完成训练,但他被限制为二范数小于等于1(位于一个高维球内),...
the learning performance of previous methods may be degraded seriously due to the complex non-Gaussian noise and the insufficiency of a prior knowledge on variable structure. To tackle this problem, we propose a new class of a...
The architecture integrates two different DNNs, including the regressive denoising DNN and the discriminative recognition DNN, into a complete multi-task structure and all the parameters can be optimized in a real joint-learning mode just from the beginning in model training. In addition, the basic...
Sparse representation has been applied to an online subspace learning-based tracking problem. To handle partial occlusion effectively, some researchers int... H Zhang,S Hu,J Yu - 《Journal of Electronic Imaging》 被引量: 1发表: 2015年 Visual tracking via robust multi-task multi-feature joint ...
3.3.1. Standard multi-task learning image-20220407222910382 Two inputs and two outputs: T-F mask network's input: log-magnitude spectrum DOA network's input: the phase spectrum which is multiplied by the predicted mask Two outputs are the estimated T-F mask and the DOA classification Loss is...