In the recommendation system, collaborative filtering methods based on graph convolution network can explicitly model interaction between nodes of user–item bipartite and effectively use higher-order neighbor information. However, its representations are very susceptible to noise interaction. response this problem, SGL explored self-supervised learning improve robustness GCN. Nevertheless, cont...

We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can be applied to both reinforcement and supervised learning. The key idea is to learn a meta-critic: an action-value function neural network that learns to criticise any actor trying to solve any specified task. For sup...

Biomedical image segmentation plays a significant role in computer-aided diagnosis. However, existing CNN based methods rely heavily on massive manual annotations, which are very expensive and require huge human resources. In this work, we adopt coarse-to-fine strategy propose self-supervised correction learning paradigm for semi-supervised biomedical segmentation. Specifically, design dual-tas...

Modern deep learning models require large amounts of accurately annotated data, which is often difficult to satisfy. Hence, weakly supervised tasks, including object localization (WSOL) and detection (WSOD), have recently received attention in the computer vision community. In this paper, we motivate propose foreground (WSFL) task by showing that both WSOL WSOD can be greatly improved if ground...

There has recently been a large effort in using unlabeled data in conjunction with labeled data in machine learning. Semi-supervised learning and active learning are two well-known techniques that exploit the unlabeled data in the learning process. In this work, the active learning is used to query a label for an unlabeled data on top of a semisupervised classifier. This work focuses on the que...

Object class recognition is an active topic in computer vision still presenting many challenges. In most approaches, this task is addressed by supervised learning algorithms that need a large quantity of labels to perform well. This leads either to small datasets (< 10, 000 images) that capture only a subset of the real-world class distribution (but with a controlled and verified labeling proce...

A popular approach to apprenticeship learning (AL) is to formulate it as an inverse reinforcement learning (IRL) problem. The MaxEnt-IRL algorithm successfully integrates the maximum entropy principle into IRL and unlike its predecessors, it resolves the ambiguity arising from the fact that a possibly large number of policies could match the expert’s behavior. In this paper, we study an AL sett...