Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective.

Fault diagnosis of a thermal system under varying operating conditions is of great importance for the safe and reliable operation of a power plant involved in peak shaving. However, it is a difficult task due to the lack of sufficient labeled data under some operating conditions. In practical applications, the model built on labeled data under one operating condition will be extended to such operating conditions. Data distribution discrepancy can be triggered by variation of operating conditions and may degenerate the performance of the model. Considering the fact that data distributions are different but related under different operating conditions, this paper proposes a hierarchical deep domain adaptation (HDDA) approach to transfer a classifier trained on labeled data under one loading condition to identify faults with unlabeled data under another loading condition. In HDDA, a hierarchical structure is developed to reveal effective information for final diagnosis by layer-wisely capturing representative features. HDDA learns domain-invariant and discriminative features with the hierarchical structure by reducing distribution discrepancy and preserving discriminative information hidden in raw process data. For practical applications, the taguchi method is used to obtain the optimized model parameters. Experimental results and comprehensive comparison analysis demonstrate its superiority.

In data mining, large differences between multi-class distributions regarded as class imbalance issues have been known to hinder the classification performance. Unfortunately, existing sampling methods have shown their deficiencies such as causing the problems of over-generation and over-lapping by oversampling techniques, or the excessive loss of significant information by undersampling techniques. This paper presents three proposed sampling approaches for imbalanced learning: the first one is the entropy-based oversampling (EOS) approach; the second one is the entropy-based undersampling (EUS) approach; the third one is the entropy-based hybrid sampling (EHS) approach combined by both oversampling and undersampling approaches. These three approaches are based on a new class imbalance metric, termed entropy-based imbalance degree (EID), considering the differences of information contents between classes instead of traditional imbalance-ratio. Specifically, to balance a data set after evaluating the information influence degree of each instance, EOS generates new instances around difficult-to-learn instances and only remains the informative ones. EUS removes easy-to-learn instances. While EHS can do both simultaneously. Finally, we use all the generated and remaining instances to train several classifiers. Extensive experiments over synthetic and real-world data sets demonstrate the effectiveness of our approaches.

Recent development in computing power has resulted in performance improvements on holistic(none-occluded) person Re-Identification (ReID) tasks. Nevertheless, the precision of the recent research will diminish when a pedestrian is obstructed by obstacles. Within the realm of 2D space, the loss of information from obstructed objects continues to pose significant challenges in the context of person ReID. Person is a 3D non-grid object, and thus semantic representation learning in only 2D space limits the understanding of occluded person. In the present work, we propose a network based on 3D multi-view learning, allowing it to acquire geometric and shape details of an occluded pedestrian from 3D space. Simultaneously, it capitalizes on advancements in 2D-based networks to extract semantic representations from 3D multi-views. Specifically, the surface random selection strategy is proposed to convert images of 2D RGB into 3D multi-views. Using this strategy, we build four extensive 3D multi-view data collections for person ReID. After that, Pedestrian 3D Shape Understanding for Person Re-Identification via Multi-View Learning(MV-3DSReID), is proposed for identifying the person by learning person geometry and structure representation from the groups of multi-view images. In comparison to alternative data formats (e.g., 2D RGB, 3D point cloud), multi-view images complement each other’s detailed features of the 3D object by adjusting rendering viewpoints, thus facilitating a more comprehensive understanding of the person for both holistic and occluded ReID situations. Experiments on occluded and holistic ReID tasks demonstrate performance levels comparable to state-of-the-art methods, validating the effectiveness of our proposed approach in tackling challenges related to occlusion. The code is available at https://github.com/hangjiaqi1/MV-TransReID.

Fault diagnosis that identifies the root of the abnormal status is of great importance to eliminate faults in the complex chemical processes. Many data-driven fault diagnosis models ignore different faults that occur with varied frequencies in chemical plants, and they need a complete retraining process with the arrival of new fault modes. In this article, a novel incremental imbalance modified convolutional neural network is proposed to solve the aforementioned issues. The proposed method employs an imbalance modified method to extract the valuable information from the imbalance data, and generate new samples. After that, a local hyperplane-based dynamic Relief is designed to reduce the dimension of the chemical data and simplify the complex learning process. Finally, for the arrival of new fault modes, the proposed method is prompted in an incremental hierarchical way. Unlike the traditional models that are trained on static data, the proposed method inherits the existing knowledge and updates itself to include new coming fault classes. The proposed method is utilized in a simulated process and a real industrial process. Experimental results illustrate that the proposed method is better than the existing methods and has significant robustness and reliability in chemical fault diagnosis.