Cognitive psychology Task-specific learning supports control over attentional capture THE UNIVERSITY OF IOWA Shaun P. Vecera CosmanJoshua DanielThere is more information in the visual environment than we can process at a given time, and as a result selective attention mechanisms have developed that ...
S. Huang, "Learning a task-specific deep architecture for clustering," in Proc. of SIAM Int. Conf. on Data Mining, Miami, Florida, May 2015, pp. 369-377. 2, 3Z. Wang, S. Chang, J. Zhou, M. Wang, and T. S. Huang, "Learning a task-specific deep architecture for clustering,"...
Conclusions: This study suggests that optimal transfer learning for medical segmentation is achieved with a similar task and domain for pre-training. As a result, CNNs can be effectively pre-trained on smaller datasets by selecting a source domain and task similar to the target domain and task....
Activated neurons express immediate-early genes, such as Arc. Expression of Arc in the hippocampal granule cell layer, an area crucial for spatial learning and memory, is increased during acquisition of spatial learning; however, it is unclear whether this effect is related to the task-specific l...
In order to address the issue, we propose a task-specific contrastive learning (TSC) model for few-shot scene classification of remote sensing images, which aims to enhance the scene classification performance with fewer labeled samples. Specifically, a self-attention and mutual-attention module (...
A novel machine learning system is presented which is used to acquire knowledge relating to a specific task. Learned feedback from high-level to low-level processes is introduced as a means of achieving robust task-specific segmentation. The system has been implemented and trained on a number ...
And even after we’ve collected an initial set of labels as ground truth or to finetune evaluation models, we’ll want to collect more labels—via active learning—to continuously improve. Taking the example of a classification eval, we can select instances to annotate based on the need to:...
5.2.3 Combining DTRN with Different Multi-Task Learning Models 在这部分研究中,我们关注于任务特定的底层表示,这与现有的多任务学习(MTL)模型是正交的。我们通过将 DTRN 与不同的 MTL 模型结合在推荐系统(RS)中,展示了其有效性。表 7 的结果表明,DTRN 输出的任务特定底层表示为每个任务提供了更强的能力,...
learning (ARL) algorithms that generate curricula forjointly training reset and forward policies. While their curriculacan reduce the number of required manual resets by taking intoaccount the agent’s learning progress, they rely on task-specif i cknowledge, such as predef i ned initial states ...
Whole slide image (WSI) classification is a critical task in computational pathology, requiring the processing of gigapixel-sized images, which is challenging for current deep-learning methods. Current state of the art methods are based on multi-instance learning schemes (MIL), which usually rely ...