Continual learning requires machine learning models to continuously acquire new knowledge in dynamic environments while avoiding the forgetting of previous knowledge. Prompt-based continual learning methods effectively address the issue of catastrophic forgetting through prompt expansion and selection. However, existing approaches often suffer from low accuracy in prompt selection, which can result in the model receiving biased knowledge and making biased predictions. To address this issue, we propose the Multiple Queries with Multiple Keys (MQMK) prompt matching paradigm for precise prompt selection. The goal of MQMK is to select the prompts whose training data distribution most closely matches that of the test sample. Specifically, Multiple Queries enable precise breadth search by introducing task-specific knowledge, while Multiple Keys perform deep search by representing the feature distribution of training samples at a fine-grained level. Each query is designed to perform local matching with a designated task to reduce interference across queries. Experiments show that MQMK enhances the prompt matching rate by over 30\% in challenging scenarios and achieves state-of-the-art performance on three widely adopted continual learning benchmarks. The code is available at https://github.com/DunweiTu/MQMK.

In conventional recognition tasks, models are only trained to recognize learned targets, but it is usually difficult to collect training examples of all potential categories. In the testing phase, when models receive test samples from unknown classes, they mistakenly classify the samples into known classes. Open set recognition (OSR) is a more realistic recognition task, which requires the classifier to detect unknown test samples while keeping a high classification accuracy of known classes. In this paper, we study how to improve the OSR performance of deep neural networks from the perspective of representation learning. We employ supervised contrastive learning to improve the quality of feature representations, propose a new supervised contrastive learning method that enables the model to learn from soft training targets, and design an OSR framework on its basis. With the proposed method, we are able to make use of label smoothing and mixup when training deep neural networks contrastively, so as to improve both the robustness of outlier detection in OSR tasks and the accuracy in conventional classification tasks. We validate our method on multiple benchmark datasets and testing scenarios, achieving experimental results that verify the effectiveness of the proposed method.

Image clustering is more challenging than image classification. Without supervised information, current deep learning methods are difficult to be directly applied to image clustering problems. Image clustering needs to deal with three main problems: 1) the curse of dimensionality caused by highdimensional image data; 2) extracting the effective image features; 3) combining feature extraction, dimensionality reduction and clustering. In this paper, we propose a new clustering framework called Deep Embedded Dimensionality Reduction Clustering (DERC) via Probability-Based Triplet Loss, which effectively solves the above issues. To the best of our knowledge, the DERC is the first framework that effectively combines image embedding, dimensionality reduction, and clustering into the image clustering process. We also propose to incorporate a novel probability-based triplet loss measure to retrain the DERC network as a unified framework. By integrating the reconstruction loss and the probability-based triplet loss, we can improve the image clustering accuracy. Extensive experiments show that our proposed methods outperform state-of-the-art methods on many commonly used datasets.

Clustering is an important technique widely used in many areas such as machine learning, pattern recognition, data analysis etc. Data stream clustering is a branch of clustering that draws much attention in recent years, where data objects are processed as an ordered sequence. In this paper, we propose an unsupervised learning neural network named Density Based Self Organizing Incremental Neural Network(DenSOINN) for data stream clustering tasks. DenSOINN is a self organizing competitive network that grows incrementally to learn suitable nodes to fit the distribution of learning data, combining on-line unsupervised learning and topology learning by means of competitive Hebbian learning rule [19]. By adopting a density-based clustering mechanism, DenSOINN can discover arbitrarily shaped clusters and diminish the negative effect of noise. In addition, we adopt a self-adaptive distance framework to obtain good performance for learning unnormalized input data. Experiments show that the DenSOINN can achieve high standard performance equally on both raw data and normalized data.