To leverage the copious amount of data from source tasks and overcome the
scarcity of the target task samples, representation learning based on
multi-task pretraining has become a standard approach in many applications.
However, up until now, most existing works design a source task selection
strategy from a purely empirical perspective. Recently, \citet{chen2022active}
gave the first active multi-task representation learning (A-MTRL) algorithm
which adaptively samples from source tasks and can provably reduce the total
sample complexity using the L2-regularized-target-source-relevance parameter
$\nu^2$. But their work is theoretically suboptimal in terms of total source
sample complexity and is less practical in some real-world scenarios where
sparse training source task selection is desired. In this paper, we address
both issues. Specifically, we show the strict dominance of the
L1-regularized-relevance-based ($\nu^1$-based) strategy by giving a lower bound
for the $\nu^2$-based strategy. When $\nu^1$ is unknown, we propose a practical
algorithm that uses the LASSO program to estimate $\nu^1$. Our algorithm
successfully recovers the optimal result in the known case. In addition to our
sample complexity results, we also characterize the potential of our
$\nu^1$-based strategy in sample-cost-sensitive settings. Finally, we provide
experiments on real-world computer vision datasets to illustrate the
effectiveness of our proposed method.

传统的多任务学习方法存在训练样本稀缺问题，该研究首次提出了基于主动学习的多任务表示学习算法，通过 LASSO 程序来估计适用于未知情况下的 L1 - 正则化相关性方法，取得了良好的实验效果。

Lasso 改进的主动多任务表示学习

Improved Active Multi-Task Representation Learning via Lasso

Although many studies have successfully applied transfer learning to medical
image segmentation, very few of them have investigated the selection strategy
when multiple source tasks are available for transfer. In this paper, we
propose a prior knowledge guided and transferability based framework to select
the best source tasks among a collection of brain image segmentation tasks, to
improve the transfer learning performance on the given target task. The
framework consists of modality analysis, RoI (region of interest) analysis, and
transferability estimation, such that the source task selection can be refined
step by step. Specifically, we adapt the state-of-the-art analytical
transferability estimation metrics to medical image segmentation tasks and
further show that their performance can be significantly boosted by filtering
candidate source tasks based on modality and RoI characteristics. Our
experiments on brain matter, brain tumor, and white matter hyperintensities
segmentation datasets reveal that transferring from different tasks under the
same modality is often more successful than transferring from the same task
under different modalities. Furthermore, within the same modality, transferring
from the source task that has stronger RoI shape similarity with the target
task can significantly improve the final transfer performance. And such
similarity can be captured using the Structural Similarity index in the label
space.

本文提出了一种基于先验知识和可转移性的框架来选择最好的源任务，在收集到的多个脑图像分割任务中进行选择，以提高给定目标任务的转移学习性能。在脑物质、脑肿瘤和白质高信号异常分割数据集上进行的实验表明，从相同模态下的不同任务转移通常比从不同模态下的同一任务转移更成功。此外，在相同模态下，从具有与目标任务更强的 RoI 形状相似性的源任务转移可以显着提高最终的转移性能，并且可以使用标签空间中的结构相似度指数来捕获这种相似性。