Considering the actual situation that we do not know the correlation between tasks in advance, we use a robust multitask feature learning (rMTFL) method to select a group of features among correlated measures and provide additional information by identifying outlier tasks at the same time. Then,...
Transfer learning has been successfully used in the early diagnosis of Alzheimer’s disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from al...
虽然sklearn中的MultiTaskLasso也是这样的目标函数,并且使用了坐标下降法来求解,但是当目标函数中的损失函数也用L2,1范数时我又懵圈了。 正当我琢磨是不是能把两部分合在一起求解一个L2,1范数时(其实是数... 查看原文 基于L2,1范数的特征选择方法 via Joint L2,1-Norms Minimization,NIPS,pp.1813-1821, ...
The MTMVT employs visual cues such as color, edge, and texture as complementary features to intensity in the target appearance representation, and combines a multi-view representation with a robust multi-task learning to solve feature fusion tracking problems. To reduce computational demands, feature...
Multi-task Learning with Coarse Priors forRobust Part-aware Person Re-identif i cationChangxing Ding*, Member, IEEE, Kan Wang*, Pengfei Wang, and Dacheng Tao, Fellow, IEEEAbstract—Part-level representations are important for robust person re-identif i cation (ReID), but in practice feature ...
更适用于Multi-task Methodology 针对多任务 TD-MPC基础上多加了一个learnable task embedding e,用于多任务训练时学习区分不同任务的信息,对于e没有设计额外的损失函数,就是直接和TD-MPC的输入concat了一下。e也会辅助encoder学到语义上与任务更相关的表征,当在单任务上finetune时直接把e设为随机的向量就可以(...
HDC performs a learning task after mapping data into high-dimensional space. This encoding is performed using a set of pre-generated base vectors. HDC is well suited to address several learning tasks in IoT systems as: (i) HDC is computationally efficient and amenable to hardware level ...
Multi-proxy feature learning for robust fine-grained visual recognition 2023, Pattern Recognition Citation Excerpt : For instance, on a noisy training set, more training iterations commonly degrade the final performance. Some works [14,15] use disagreement to refine the optimization step for noisy la...
multiguard: provably robust multi-label classification against adversarial examples [Paper] unsupervised model selection for time-series anomaly detection [Paper] perceptual attacks of no-reference image quality models with human-in-the-loop [Paper] robust q -learning algorithm for markov decision...
Cancer classification Hybrid model CNN Transformer Feature fusion Multi-task learning 1. Introduction With the advent of a large volume of whole-slide images (WSIs) and advances in artificial intelligence (AI), digital and computational pathology has grown rapidly and shown great promise to revolutioni...