Masked Two-channel Decoupling Framework for Handling Incomplete Multi-view Weak Multi-label Data

Kernekoncepter

A novel masked two-channel decoupling framework (MTD) that can effectively handle incomplete multi-view and weak multi-label data by decoupling the feature representation into shared and view-proprietary channels, and employing a cross-channel contrastive loss, random fragment masking, and label-guided graph regularization.

Resumé

The paper proposes a masked two-channel decoupling framework (MTD) for the task of incomplete multi-view weak multi-label learning (iMvWMLC). The key innovations are:

Decoupling the single-channel view-level representation into a shared representation and a view-proprietary representation using two-channel encoders. A cross-channel contrastive loss is designed to enhance the semantic property of the two channels.

Introducing a random fragment masking strategy to the input data, inspired by the success of masking mechanisms in image and text analysis, to improve the learning ability of the encoders.

Exploiting supervised label information to design a label-guided graph regularization loss, helping the extracted embedding features preserve the geometric structure among samples.

The proposed MTD framework is fully adaptable to arbitrary view and label absences while also performing well on the ideal full data. Extensive experiments on five benchmark datasets demonstrate the effectiveness and advancement of the MTD framework compared to state-of-the-art methods.

Statistik

The number of samples in the datasets ranges from 4999 to 25000, and the number of categories ranges from 20 to 291.
50% of instances on each view are randomly selected as unavailable instances, and 50% of the positive and negative tags for each category are set to be unknown.
70% of samples with missing views and missing labels are randomly selected as the training set.

Citater

"To tackle these issues, we present the Masked Two-channel Decoupling framework (MTD for short), capable of handling cases where partial views and labels are both missing."
"To our best knowledge, we are the first to apply random fragment mask in the field of multi-view learning and achieve significant performance gains, which supports a new multi-view vector data enhancement mechanism for the communication."
"Different from existing graph regularization based approaches, we utilize supervised label information to build a more reliable topological graph, inducing the embedding features extracted by the encoders to preserve the geometric structure among samples."

Vigtigste indsigter udtrukket fra

Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning

by Chengliang L... kl. arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17340.pdf

Dybere Forespørgsler

How can the proposed MTD framework be extended to handle more complex missing patterns, such as non-random missing views and labels

The proposed Masked Two-channel Decoupling (MTD) framework can be extended to handle more complex missing patterns by incorporating additional mechanisms to address non-random missing views and labels. One approach could involve introducing a mechanism to identify and handle specific patterns of missing data. For non-random missing views, the framework could include a mechanism to detect the patterns of missing views and adjust the learning process accordingly. This could involve incorporating a mechanism to impute missing views based on the available data or leveraging information from other views to compensate for the missing ones.
Similarly, for non-random missing labels, the framework could incorporate techniques from semi-supervised learning to predict missing labels based on the available information. This could involve leveraging the relationships between samples and labels to infer the missing labels. Additionally, techniques such as label propagation or label correlation modeling could be integrated into the framework to improve the handling of missing labels.
By incorporating these additional mechanisms, the MTD framework can be extended to handle more complex missing patterns, providing a more robust and comprehensive solution for incomplete multi-view and weak multi-label data.

What are the potential limitations of the cross-channel contrastive loss, and how can it be further improved to better capture the relationships between shared and view-proprietary features

The cross-channel contrastive loss in the MTD framework, while effective in encouraging consistency between shared features and distinguishing between view-proprietary features, may have limitations in capturing complex relationships between these features. One potential limitation is the sensitivity of the loss function to the choice of hyperparameters, such as the weighting factors for positive and negative pairs. If these factors are not appropriately tuned, it may lead to suboptimal performance or convergence issues.
To improve the cross-channel contrastive loss, one approach could be to incorporate adaptive weighting mechanisms that dynamically adjust the importance of positive and negative pairs during training. This adaptive weighting could be based on the similarity or dissimilarity between instances, allowing the loss function to focus more on challenging pairs that contribute significantly to the learning process.
Additionally, integrating a regularization term that encourages diversity within each channel while maintaining consistency across channels could help address the limitations of the cross-channel contrastive loss. By promoting diversity within each channel, the framework can learn more robust and discriminative representations, enhancing the overall performance of the model.

Can the random fragment masking strategy be combined with other data augmentation techniques to further boost the performance of the MTD framework on incomplete multi-view and weak multi-label data

The random fragment masking strategy in the MTD framework can be combined with other data augmentation techniques to further enhance the performance on incomplete multi-view and weak multi-label data. One potential approach is to integrate traditional data augmentation techniques, such as rotation, scaling, and flipping, with the random fragment masking strategy. By applying a combination of these techniques, the model can learn more robust and generalized representations from the incomplete data.
Furthermore, techniques such as mixup, cutmix, or dropout can be incorporated to introduce additional variations in the data and improve the model's ability to generalize to unseen samples. By combining random fragment masking with these techniques, the model can learn more invariant and discriminative features, leading to improved performance on incomplete and weakly labeled data.
Moreover, self-supervised learning approaches, such as contrastive learning or generative adversarial networks, can be integrated with the random fragment masking strategy to learn more informative representations from the incomplete data. By leveraging the complementary strengths of these techniques, the MTD framework can further boost its performance on challenging multi-view and weak multi-label learning tasks.