Co-training, extended from self-training, is one of the frameworks for semi-supervised learning. Without natural split of features, single-view co-training works at the cost of training extra classifiers, where the algorithm should be delicately designed to prevent individual classifiers from collapsing into each other. To remove these obstacles which deter the adoption of single-view co-training, we present a simple and efficient algorithm Multi-Head Co-Training. By integrating base learners into a multi-head structure, the model is in a minimal amount of extra parameters. Every classification head in the unified model interacts with its peers through a “Weak and Strong Augmentation” strategy, in which the diversity is naturally brought by the strong data augmentation. Therefore, the proposed method facilitates single-view co-training by 1). promoting diversity implicitly and 2). only requiring a small extra computational overhead. The effectiveness of Multi-Head Co-Training is demonstrated in an empirical study on standard semi-supervised learning benchmarks.

Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various human emotions from heterogeneous visual, audio and text modalities. Previous methods mainly focus on projecting multiple modalities into a common latent space and learning an identical representation for all labels, which neglects the diversity of each modality and fails to capture richer semantic information for each label from different perspectives. Besides, associated relationships of modalities and labels have not been fully exploited. In this paper, we propose versaTile multi-modAl learning for multI-labeL emOtion Recognition (TAILOR), aiming to refine multi-modal representations and enhance discriminative capacity of each label. Specifically, we design an adversarial multi-modal refinement module to sufficiently explore the commonality among different modalities and strengthen the diversity of each modality. To further exploit label-modal dependence, we devise a BERT-like cross-modal encoder to gradually fuse private and common modality representations in a granularity descent way, as well as a label-guided decoder to adaptively generate a tailored representation for each label with the guidance of label semantics. In addition, we conduct experiments on the benchmark MMER dataset CMU-MOSEI in both aligned and unaligned settings, which demonstrate the superiority of TAILOR over the state-of-the-arts.

With the rapid development of Artificial Intelligence (AI), a long line of past papers have shown concerns about the data distribution shifts problem in image classification models via deep learning. Moreover, there is also an Out-of-Distribution (OOD) problem in perturbation-based explanation methods for DNNs ineXplainable Artificial Intelligence (XAI), because the generated perturbation samples may be not the same distribution as the original dataset. We explore the limitations of post-hoc Learning to Explain (L2X) explanation methods that use approximators to mimic the behavior of DNNs. We propose a training pipeline called Feature Importance eXplanation(-based) Calibration (FIXC), which efficiently extracts feature importance without using imitations of existing DNNs. We use feature importance as additional information to calibrate data distribution shifts. The evaluation of the corrupted dataset and DNNs benchmarks shows that the FIXC effectively improves the classification accuracy of corrupted images. Experiments on popular vision datasets show that the FIXC outperforms state-of-the-art methods on calibration metrics While the training pipeline provides a calibrated feature importance explanation. We also provide an analysis of our method based on game interaction theory.

Exploiting label correlation is crucially important in multi-label learning, where each instance is associated with multiple labels simultaneously. Multi-label learning is more complex than single-label learning for that the labels tend to be correlated. Traditional multi-label learning algorithms learn independent classifiers for each label and employ ranking or threshold on the classification results. Most existing methods take label correlation as prior knowledge, which have worked well, but they failed to make full use of label dependency. As a result, the real relationship among labels may not be correctly characterized and the final prediction is not explicitly correlated. To address these problems, we propose a novel high-order multi-label learning algorithm of Label collAboration based Multi-laBel learning (LAMB). With regard to each label, LAMB utilizes collaboration between its own prediction and the prediction of other labels. Extensive experiments on various datasets demonstrate that our proposed LAMB algorithm achieves superior performance over existing state-of-the-art algorithms. In addition, one real-world dataset of channelrhodopsins chimeras is assessed, which would be of great value as pre-screen for membrane proteins function.

In Multi-View Multi-Label (MVML) learning, each instance is represented with several feature vectors and associated with multiple labels simultaneously. Existing models are mainly built on a shared latent space among multiple views and tend to select features that are distinguishable for all labels. However, they ignore the distinctive characteristic of each view and fail to extract the discriminative information related to corresponding labels. To address the above challenges, we proposed a novel approach named Label-specific Alignment with Adversarial Multi-view Representation (LAAMR). For one thing, shared and private information is extracted with adversarial training and orthogonal constraint, which ensures complementarity and consistency among multiple views. For another, label semantic information is well captured with multi-head attention, and then utilized as the guidance to exploit label-view dependence with alignment attention, resulting in label-specific representations. Experiments on various MVML datasets validate the effectiveness of LAAMR.

Wastewater treatment is an important link to solve the pollution of water resources and realize the recycling of water resources. Predicting effluent water quality by influent water quality is an important part of the actual production process. The existing forecasting methods have the problem of low accuracy and efficiency, and cannot be used well in the actual production process. To tackle the above issues, we propose a novel method to use the transformer model to fit the wastewater treatment process which improves the prediction of effluent water quality. Experiments on various datasets validate the effectiveness of our proposed method. In the worst case, the sample prediction accuracy rate of our model achieves as high as 96.8%. At the same time, our model training with thousands of real production data, the training time is not more than two minutes. As far as we know, we are the first to use the transformer model in wastewater treatment tasks and achieve fast and accurate predictions. In the future, we will apply our model in actual production to achieve accurate predictions in dynamic data update scenarios.

With the acceleration of urbanization, there is an explosive growth trend of the stroke burden in China. Consequently, it is necessary to adopt a regular screen to reduce brain damage and other complications. For one thing, the risk factors screened can be divided into 6 subsets: medical history, family history, fasting blood, lifestyle, physical characteristics, and demographic information. For another, the screen results can be categorized into 5 groups: normal carotid plaque, thickening, unstable plaque, and arterial stenosis. To address the above problems, Multi-View Multi-Label (MVML) learning provides a fundamental framework for handling such tasks, which treats the risk factors as multi-view data and the screen results as multi-label data. Besides, it is a challenging issue of how to effectively utilize view correlations and label correlations in MVML learning. In this paper, we propose a novel Multi-view Multi-label Learning with Dual-Attention Networks (MML-DAN) for stroke screen, which can strike a balance between label-specific views and label correlations. Meanwhile, the attention mechanism aims to focus on the most pertinent information of the corresponding labels rather than using all available risk factors equally. Experiments on one real-world dataset of stroke screen demonstrate the superiority of the proposed MML-DAN for stroke screen prediction. To further validate the effectiveness and generalization capability of MML-DAN, extensive experiments on 5 benchmark MVML datasets show that MML-DAN significantly outperforms other state-of-the-art MVML algorithms.

Transfer learning has been demonstrated to be successful and essential in diverse applications, which transfers knowledge from related but different source domains to the target domain. Online transfer learning(OTL) is a more challenging problem where the target data arrive in an online manner. Most OTL methods combine source classifier and target classifier directly by assigning a weight to each classifier, and adjust the weights constantly. However, these methods pay little attention to reducing the distribution discrepancy between domains. In this paper, we propose a novel online transfer learning method which seeks to find a new feature representation, so that the marginal distribution and conditional distribution discrepancy can be online reduced simultaneously. We focus on online transfer learning with multiple source domains and use the Hedge strategy to leverage knowledge from source domains. We analyze the theoretical properties of the proposed algorithm and provide an upper mistake bound. Comprehensive experiments on two real-world datasets show that our method outperforms state-of-the-art methods by a large margin.

Multi-modal multi-label learning provides a fundamental framework for complex objects, which can be represented with multiple modalities and annotated with multiple labels simultaneously. Different modalities can usually provide complementary information, which may lead to improved performance. What's more, exploiting label correlations is crucially important to multi-label learning. However, most existing multi-label learning approaches do not sufficiently consider the complementary information among different modalities. In this paper, we propose a novel end-to-end deep learning framework named Rethinking Modal-oriented Label Correlations (RMLC), which sequentially polish the label prediction with each individual modality. In order to explicitly account for the correlated prediction of multiple labels, RMLC leverages an efficient sequential modal-based exploration to rethink label correlations. The final prediction of each label involves the collaboration between modal-specific prediction and the prediction of other labels based on cross-modal interaction. Comprehensive experiments on benchmark datasets validate the effectiveness and competitiveness of the proposed RMLC approach.

Integral membrane proteins (MPs) make up a large proportion of the genomes of many organisms and the amount of sequenced proteins is growing at an unprecedented pace. As a result, MPs function pre-screen is of great necessity. To be functional, MPs must be expressed and localized through a series of elaborate sub-cellular processes. It is worth noting that subtle changes in sequence may lead to drastic changes in expression and localization. In light of the above observation, taking the complementary information of sequence-based proteins into consideration, a novel boosting Dual-Modal Dual-Label (DMDL) algorithm with hypothesis reuse is proposed. On the one hand, DMDL treats sequence features and structure features as dual-modal. On the other hand, DMDL considers eukaryotic expression and plasma membrane localization as dual-label, which would be of great value as a pre-screen for MPs function. Meanwhile, two label sets interact, which can not only make the best use of two modal features, but also could effectively exploit the relationship between two label sets. An assessment of real-world channelrhodopsins (ChRs) chimeras clearly validate the effectiveness of DMDL algorithm compared with state-of-the-art algorithms. In addition, extensive experiments are performed on adapted public datasets, showing effectiveness of hypothesis reuse mechanism in DMDL.

With the emergence of diverse data collection techniques, objects in real applications can be represented as multi-modal features. What's more, objects may have multiple semantic meanings. Multi-modal and Multi-label [1] (MMML) problem becomes a universal phenomenon. The quality of data collected from different channels are inconsistent and some of them may not benefit for prediction. In real life, not all the modalities are needed for prediction. As a result, we propose a novel instance-oriented Multi-modal Classifier Chains (MCC) algorithm for MMML problem, which can make convince prediction with partial modalities. MCC extracts different modalities for different instances in the testing phase. Extensive experiments are performed on one real-world herbs dataset and two public datasets to validate our proposed algorithm, which reveals that it may be better to extract many instead of all of the modalities at hand.

Biomedical event extraction plays an important role in the field of biomedical text mining, and the event trigger detection is the first step in the pipeline process of event extraction. Event trigger can clearly indicates the occurrence of related events. There have been many machine learning based methods applied to this area already. However, most previous work have omitted two crucial points: (1) Class Difference: They simply regard non-trigger as same level class label. (2) Information Isolation: Most methods only utilize token level information. In this paper, we propose a novel model based on gate mechanism, which identifies trigger and non-trigger words in the first stage. At the same time, we also introduce additional fusion layer in order to incorporate sentence level information for event trigger detection. Experimental results on the Multi Level Event Extraction (MLEE) corpus achieve superior performance than other state-of-the-art models. We have also performed ablation study to show the effectiveness of proposed model components.