Abductive multi-instance multi-label learning for periodontal disease classification with prior domain knowledge.

Machine learning is widely used in dentistry nowadays, offering efficient solutions for diagnosing dental diseases, such as periodontitis and gingivitis. Most existing methods for diagnosing periodontal diseases follow a two-stage process. Initially, they detect and classify potential Regions of Interest (ROIs) and subsequently determine the labels of the whole images.

Class-Incremental Learning: A Survey.

Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world.

Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks.

Meta-learning has become a practical approach towards few-shot image classification, where "a strategy to learn a classifier" is meta-learned on labeled base classes and can be applied to tasks with novel classes. We remove the requirement of base class labels and learn generalizable embeddings via Unsupervised Meta-Learning (UML). Specifically, episodes of tasks are constructed with data augmentations from unlabeled base classes during meta-training, and we apply embedding-based classifiers to novel tasks with labeled few-shot examples during meta-test.

Few-Shot Learning With a Strong Teacher.

Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing works take the meta-learning approach, constructing a few-shot learner (a meta-model) that can learn from few-shot examples to generate a classifier. Typically, the few-shot learner is constructed or meta-trained by sampling multiple few-shot tasks in turn and optimizing the few-shot learner's performance in generating classifiers for those tasks.

Generalized Knowledge Distillation via Relationship Matching.

The knowledge of a well-trained deep neural network (a.k.a.

Heterogeneous Few-Shot Model Rectification With Semantic Mapping.

There still involve lots of challenges when applying machine learning algorithms in unknown environments, especially those with limited training data. To handle the data insufficiency and make a further step towards robust learning, we adopt the learnware notion Z.-H.

Learning Multiple Local Metrics: Global Consideration Helps.

Learning distance metric between objects provides a better measurement for their relative comparisons. Due to the complex properties inside or between heterogeneous objects, multiple local metrics become an essential representation tool to depict various local characteristics of examples. Different from existing methods building more than one local metric directly, however in this paper, we emphasize the effect of the global metric when generating those local ones.

What Makes Objects Similar: A Unified Multi-Metric Learning Approach.

Linkages are essentially determined by similarity measures that may be derived from multiple perspectives. For example, spatial linkages are usually generated based on localities of heterogeneous data. Semantic linkages, however, can come from even more properties, such as different physical meanings behind social relations.