Partial label learning is an important weakly supervised machine learning framework. In partial label learning, each object is described by a single instance in the input space; however, in the output space, it is associated with a set of candidate labels among which only one is valid. An int...
Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical
Then, the fully connected layer maps these extracted feature vectors to the sample label space and classifies them by constructing a classifier. For the classification, the softmax function is usually selected as the activation function of the fully connected layer, which converts the output vector...
Classification problems frequently assume that all input features are relevant; however, when dealing with large datasets, there may be no prior knowledge of the relationship between a feature and the target label. For this problem, filter and wrapper methods must be used to assess the relevance ...
The mainstream pseudo-labeling method (PL) [12] uses the model to label unlabeled samples during the training process and incorporate them into the next round of training, which can assist the model in learning the hidden information in unlabeled samples. Virtual adversarial training (VAT) ...
A unique label ID is assigned to each cluster to distinguish between the different objects. Figure 4. Each object cluster is assigned a different label ID. After obtaining the clusters corresponding to the objects on the table, the next step is to reconstruct the occluded parts. This is ...