Catastrophic forgetting, the degradation of knowledge about previously seen classes when learning new concepts from a shifting data stream, is a pitfall faced by neural network learning in open environments. Recent research on continual image classification usually relies on storing samples or prototypes to resist this forgetting. We find that during acquiring knowledge of the new classes, the features of old classes gradually disperse, which leads to confusion of features between classes and makes them difficult to discriminate. Coping with feature dispersion would be a key consideration in resisting catastrophic forgetting, which has been neglected in previous works. To this end, we try to address this issue from two perspectives. First, we propose a dispersing feature generation mechanism, which generates pseudo-features based on the pre-pooling prototypes of the old classes to simulate feature dispersion and remind the classifier to adjust the decision boundary. Second, we design a consistent alignment constraint to alleviate the severity of feature dispersion by maintaining consistency in the hidden states of different depths when aligning the current model with the previous model. Extensive experimental results on various benchmarks show the superiority of our proposed method.

Vertical Federated Learning (VFL) has emerged as a crucial privacy-preserving learning paradigm that involves training models using distributed features from shared samples. However, the performance of VFL can be hindered when the number of shared or aligned samples is limited, a common issue in mobile environments where user data are diverse and unaligned across multiple devices. Existing approaches use feature generation and pseudo-label estimation for unaligned samples to address this issue, unavoidably introducing noise during the generation process. In this work, we propose Local Enhanced Effective Vertical Federated Learning (LEEF-VFL), which fully utilizes unaligned samples in the local learning before collaboration. Unlike previous methods that overlook private labels owned by each client, we leverage these private labels to learn from all local samples, constructing robust local models to serve as solid foundations for collaborative learning. Additionally, we reveal that the limited number of aligned samples introduces distribution bias from global data distribution. In this case, we propose to minimize the distribution discrepancies between the aligned samples and the global data distribution to enhance collaboration. Extensive experiments demonstrate the effectiveness of LEEF-VFL in addressing the challenges of limited aligned samples, making it suitable for VFL in mobile computing environments.

The chain-of-thought technique has been received well in multi-modal tasks. It is a step-by-step linear reasoning process that adjusts the length of the chain to improve the performance of generated prompts. However, human thought processes are predominantly non-linear, as they encompass multiple aspects simultaneously and employ dynamic adjustment and updating mechanisms. Therefore, we propose a novel Aggregation-Graph-of-Thought (AGoT) mechanism for soft-prompt tuning in multi-modal representation learning. The proposed AGoT models the human thought process not only as a chain but also models each step as a reasoning aggregation graph to cope with the overlooked multiple aspects of thinking in single-step reasoning. This turns the entire reasoning process into prompt aggregation and prompt flow operations. Experiments show that our multi-modal model enhanced with AGoT soft-prompting achieves good results in several tasks such as text-image retrieval, visual question answering, and image recognition. In addition, we demonstrate that it has good domain generalization performance due to better reasoning.

Adapter-based parameter-efficient transfer learning has achieved exciting results in vision-language models. Traditional adapter methods often require training or fine-tuning, facing challenges such as insufficient samples or resource limitations. While some methods overcome the need for training by leveraging image modality cache and retrieval, they overlook the text modality’s importance and cross-modal cues for the efficient adaptation of parameters in visual-language models. This work introduces a cross-modal parameter-efficient approach named XMAdapter. XMAdapter establishes cache models for both text and image modalities. It then leverages retrieval through visual-language bimodal information to gather clues for inference. By dynamically adjusting the affinity ratio, it achieves cross-modal fusion, decoupling different modal similarities to assess their respective contributions. Additionally, it explores hard samples based on differences in cross-modal affinity and enhances model performance through adaptive adjustment of sample learning intensity. Extensive experimental results on benchmark datasets demonstrate that XMAdapter outperforms previous adapter-based methods significantly regarding accuracy, generalization, and efficiency.

Abstract The data stream is a dynamic collection of data that changes over time, and predicting the data class can be challenging due to sparse samples, complex interdependent characteristics between data, and random fluctuations. Accurately predicting the data stream in sparse data can create complex challenges. Due to its incremental learning nature, the neural networks suitable approach for streaming visualization. However, the high computational cost limits their applicability to high-speed streams, which has not yet been fully explored in the existing approaches. To solve these problems, this paper proposes an end-to-end dynamic separation neural network (DSN) approach based on the characteristics of data stream fluctuations, which expands the static sample at a given moment into a sequence of sample streams in the time dimension, thereby increasing the sparse samples. The Temporal Augmentation Module (TAM) can overcome these challenges by modifying the sparse data stream and reducing time complexity. Moreover, a neural network that uses a Variance Detection Module (VDM) can effectively detect the variance of the input data stream through the network and dynamically adjust the degree of differentiation between samples to enhance the accuracy of forecasts. The proposed method adds significant information regarding the data sparse samples and enhances low dimensional samples to high data samples to overcome the sparse data stream problem. In VDM the preprocessed data achieve data augmentation and the samples are transmitted to VDM. The proposed method is evaluated using different types of data streaming datasets to predict the sparse data stream. Experimental results demonstrate that the proposed method achieves a high prediction accuracy and that the data stream has significant effects and strong robustness compared to other existing approaches.

Deep clustering is a crucial task in machine learning and data mining that focuses on acquiring feature representations conducive to clustering. Previous research relies on self-supervised representation learning for general feature representations, such features may not be optimally suited for downstream clustering tasks. In this article, we introduce MICCF, a framework designed to bridge this gap and enhance clustering performance. MICCF enhances feature representations by combining mutual information constraints at different levels and employs an auxiliary alignment mutual information module for learning clustering-oriented features. To be specific, we propose a dual mutual information constraints module, incorporating minimal mutual information constraints at the feature level and maximal mutual information constraints at the instance level. This reduction in feature redundancy encourages the neural network to extract more discriminative features, while maximization ensures more unbiased and robust representations. To obtain clustering-oriented representations, the auxiliary alignment mutual information module utilizes pseudo-labels to maximize mutual information through a multi-classifier network, aligning features with the clustering task. The main network and the auxiliary module work in synergy to jointly optimize feature representations that are well-suited for the clustering task. We validate the effectiveness of our method through extensive experiments on six benchmark datasets. The results indicate that our method performs well in most scenarios, particularly on fine-grained datasets, where our approach effectively distinguishes subtle differences between closely related categories. Notably, our approach achieved a remarkable accuracy of 96.4% on the ImageNet-10 dataset, surpassing other comparison methods. The code is available at https://github.com/Li-Hyn/MICCF.git .

The rising popularity of tabular data in data science applications has led to a surge of interest in utilizing deep neural networks (DNNs) to address tabular problems. Existing deep neural network methods are not effective in handling two fundamental challenges that are inherent in tabular data: permutation invariance (where the labels remain unchanged regardless of element order) and local dependency (where predictive labels are solely determined by local features). Furthermore, given the inherent heterogeneity among elements in tabular data, effectively capturing heterogeneous feature interactions remains unresolved. In this paper, we propose a novel Multiplex Cross-Feature Interaction Network (MPCFIN) by explicitly and systematically modeling feature relations with interactive graph neural networks. Specifically, MPCFIN first learns the most relevant features associated with individual features, and merges them to form cross-feature embedding. Subsequently, we design a multiplex graph neural network to learn enhanced representation for each sample. Comprehensive experiments on seven datasets demonstrate that MPCFIN exhibits superior performance over deep neural network methods in modeling the tabular data, showcasing consistent interpretability in its cross-feature embedding module for medical diagnosis applications.

Data augmentation has undoubtedly enabled a significant leap forward in training a high-accuracy deep network. Besides the commonly used augmentation to target data, e.g., random cropping, flipping, and rotation, recent works have been dedicated to mining generalized knowledge by using multiple sources. However, along with plentiful data comes the huge data distribution gap between the target and different sources (hybrid shift). To mitigate this problem, existing methods tend to manually annotate more data. Unlike previous methods, this paper focuses on the study of learning deep models by gathering knowledge from multiple sources in a labor-free fashion and further proposes the "Multi-Alignment and Pseudo-Learning'' method, dubbed MAPLE. MAPLE constructs the multi-alignment module, which consists of multiple discriminators to align different data distributions via an adversarial process. In addition, a novel semi-supervised learning (SSL) manner is introduced to further facilitate the utility of our MAPLE. Extensive evaluations conducted on four benchmarks show the effectiveness of the proposed MAPLE, which achieves state-of-the-art performance outperforming existing methods by an obvious margin.

Summary At present, computer technology and Internet technology have been deeply integrated with the development of the Internet. This paper constructs a financial time series analysis and prediction model under the background of Internet e‐commerce development. As IT technology, data mining must be simple and fast. Therefore, starting from the model and based on the algorithm, this paper establishes a feasible serial data mining technology and theoretical system. Based on this, this paper deeply studies the data repair technology and interpolation technology such as EM algorithm for the actual more missing data, and the result is very satisfactory. Finally, this paper further studies the forward search algorithm, which is based on clustering to pretreat can complete the calculation process in a shorter time, reduce the possibility of errors, and improve efficiency.