Li, D., Yang, Y., Song, Y., Hospedales, T.M.: Learning to generalize: Meta-learning for domain generalization. In: Conference of the Association for the Advancement of Artificial Intelligence (AAAI) (2018)Li et al., Learning to generalize: Meta-learning for domain generalization, AAAI ...
但是meta learning同样存在一些缺陷。一是虽然可能训练得到的模型对distribution shift不那么敏感,但仍不能避免模型对源域数据过拟合。二是模型每一层更新都要求两次梯度,计算效率自然会慢。 以上是我看综述的阅读笔记。 Domain Generalization in Vision: A Surveyarxiv.org/abs/2103.02503发布...
关于为什么这里会有两个目标,可以参考Learning to Generalize:Meta-Learning for Domain Generalization这篇论文。可以看出来这里只是用了最基本的交叉熵损失,但是交叉熵损失除了对Ss进行计算,还对增广的源域数据进行了计算。meta-training loss用于提升第s个源域的分类性能,相当于提升discriminability。meta-objective loss用...
Thus, in our work, meta-learning is adopted for domain generalization by further optimizing model weights via a meta-optimizer to overcome the shortcomings of few-shot learning. Recent work by Ref. [14] used the gradient-based meta-learning algorithm known as Model Agnostic Meta Learning (MAML...
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习 machine-learningdeep-learningpapersurveystyle-transfertheorytransfer-learningpapersrepresentation-learningunsupervised-learningtutorial-codedomain-adaptationgeneralizationtr...
In our approach, we incorporate dual tasks into our framework to enhance the model’s generalization capability and domain adaptability. Specifically, for the support set and query set of a single task, we generate augmented samples through data augmentation techniques. These samples help the model ...
我们知道现在深度学习在使用大型数据集掌握一项任务(检测,分类等)方面取得了巨大的成功,但这并不是真正我们追求的“人工智能”。具体来说,我们可能训练了一个能做物理题很高分的学生,但是他也只能做物理题而已,面对数学题他只能吞下零分的命运;其次,在面对新的任务(数学题)的时候,我们的学生仍然需要大量的数据(数...
The generalization capability of machine learning models, which refers to generalizing the knowledge for an "unseen" domain via learning from one or multiple seen domain(s), is of great importance to develop and deploy machine learning applications in the real-world conditions. Domain Generalization ...
Similarity-based Meta-Representation Learning for Domain Generalization - dongzhiq/similarity-based-meta-representation-learning
since the approach actually undergoes a few gradient steps for a novel task, it allows the model to perform better on out-of-sample data, and hence achieves better generalization. This behavior can be attributed to the central assumption of meta-learning: that the tasks are inherently related ...