Few-shot Partial Multi-view Learning

• URL: http://arxiv.org/abs/2105.02046v4
• Date: Thu, 18 May 2023 13:25:25 GMT
• Title: Few-shot Partial Multi-view Learning
• Authors: Yuan Zhou, Yanrong Guo, Shijie Hao, Richang Hong, Jiebo Luo
• Abstract summary: We propose a new task called few-shot partial multi-view learning. It focuses on overcoming the negative impact of the view-missing issue in the low-data regime. We conduct extensive experiments to evaluate our method.
• Score: 103.33865779721458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness.

Related papers

• Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification [25.764838008710615]
  We present a task-augmented cross-view imputation network (TACVI-Net) for handling partial multi-view incomplete multi-label classification. In the first stage, we leverage the information bottleneck theory to obtain a discriminative representation of each view. In the second stage, an autoencoder based multi-view reconstruction network is utilized to extract high-level semantic representation.
  arXiv  Detail & Related papers  (2024-09-12T10:56:11Z)
• Regularized Contrastive Partial Multi-view Outlier Detection [76.77036536484114]
  We propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD) In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-02T14:34:27Z)
• CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network [53.72046586512026]
  We propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net) It captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training.
  arXiv  Detail & Related papers  (2024-03-28T15:45:03Z)
• Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity [9.811378971225727]
  This paper extends the current research into missing modalities to the low-data regime. It is often expensive to get full-modality data and sufficient annotated training samples. We propose to use retrieval-augmented in-context learning to address these two crucial issues.
  arXiv  Detail & Related papers  (2024-03-14T14:19:48Z)
• Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images [6.817740582240199]
  Multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. The issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification.
  arXiv  Detail & Related papers  (2023-01-02T12:27:55Z)
• Uncertainty-Aware Multi-View Representation Learning [53.06828186507994]
  We devise a novel unsupervised multi-view learning approach, termed as Dynamic Uncertainty-Aware Networks (DUA-Nets) Guided by the uncertainty of data estimated from the generation perspective, intrinsic information from multiple views is integrated to obtain noise-free representations. Our model achieves superior performance in extensive experiments and shows the robustness to noisy data.
  arXiv  Detail & Related papers  (2022-01-15T07:16:20Z)
• TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification [24.01191516774655]
  A transductive semi-supervised incomplete multi-view TSK fuzzy system modeling method (SSIMV_TSK) is proposed to address these challenges. The proposed method integrates missing view imputation, pseudo label learning of unlabeled data, and fuzzy system modeling into a single process to yield a model with interpretable fuzzy rules. Experimental results on real datasets show that the proposed method significantly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-08T11:41:06Z)
• Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities [67.54503077766171]
  Since label information is often expensive to acquire, multi-view clustering has gained growing interest. Error-robust multi-view clustering approaches with explicit error removal formulation can be structured into five broad research categories. This survey summarizes and reviews recent advances in error-robust clustering for multi-view data.
  arXiv  Detail & Related papers  (2021-05-07T04:03:02Z)
• Batch Exploration with Examples for Scalable Robotic Reinforcement Learning [63.552788688544254]
  Batch Exploration with Examples (BEE) explores relevant regions of the state-space guided by a modest number of human provided images of important states. BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka robot.
  arXiv  Detail & Related papers  (2020-10-22T17:49:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.