Supervised Multi-Modal Fission Learning

• URL: http://arxiv.org/abs/2409.20559v1
• Date: Mon, 30 Sep 2024 17:58:03 GMT
• Title: Supervised Multi-Modal Fission Learning
• Authors: Lingchao Mao, Qi wang, Yi Su, Fleming Lure, Jing Li,
• Abstract summary: Learning from multimodal datasets can leverage complementary information and improve performance in prediction tasks. We propose a Multi-Modal Fission Learning model that simultaneously identifies globally joint, partially joint, and individual components.
• Score: 19.396207029419813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from multimodal datasets can leverage complementary information and improve performance in prediction tasks. A commonly used strategy to account for feature correlations in high-dimensional datasets is the latent variable approach. Several latent variable methods have been proposed for multimodal datasets. However, these methods either focus on extracting the shared component across all modalities or on extracting both a shared component and individual components specific to each modality. To address this gap, we propose a Multi-Modal Fission Learning (MMFL) model that simultaneously identifies globally joint, partially joint, and individual components underlying the features of multimodal datasets. Unlike existing latent variable methods, MMFL uses supervision from the response variable to identify predictive latent components and has a natural extension for incorporating incomplete multimodal data. Through simulation studies, we demonstrate that MMFL outperforms various existing multimodal algorithms in both complete and incomplete modality settings. We applied MMFL to a real-world case study for early prediction of Alzheimers Disease using multimodal neuroimaging and genomics data from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. MMFL provided more accurate predictions and better insights into within- and across-modality correlations compared to existing methods.

Related papers

• FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data [64.50893177169996]
  Fine-tuning Multimodal Large Language Models (MLLMs) with Federated Learning (FL) allows for expanding the training data scope by including private data sources. We introduce a benchmark for evaluating various downstream tasks in the federated fine-tuning of MLLMs within multimodal heterogeneous scenarios. We develop a general FedMLLM framework that integrates four representative FL methods alongside two modality-agnostic strategies.
  arXiv  Detail & Related papers  (2024-11-22T04:09:23Z)
• MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT [11.884646027921173]
  We propose MMBind, a new framework for multimodal learning on distributed and heterogeneous IoT data. We demonstrate that data of different modalities observing similar events, even captured at different times and locations, can be effectively used for multimodal training.
  arXiv  Detail & Related papers  (2024-11-18T23:34:07Z)
• Completed Feature Disentanglement Learning for Multimodal MRIs Analysis [36.32164729310868]
  Feature disentanglement (FD)-based methods have achieved significant success in multimodal learning (MML) We propose a novel Complete Feature Disentanglement (CFD) strategy that recovers the lost information during feature decoupling. Specifically, the CFD strategy not only identifies modality-shared and modality-specific features, but also decouples shared features among subsets of multimodal inputs.
  arXiv  Detail & Related papers  (2024-07-06T01:49:38Z)
• U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation [63.31007867379312]
  We introduce U3M: An Unbiased Multiscale Modal Fusion Model for Multimodal Semantics. We employ feature fusion at multiple scales to ensure the effective extraction and integration of both global and local features. Experimental results demonstrate that our approach achieves superior performance across multiple datasets.
  arXiv  Detail & Related papers  (2024-05-24T08:58:48Z)
• FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology [3.802258033231335]
  Federated Multi-Modal (FedMM) is a learning framework that trains multiple single-modal feature extractors to enhance subsequent classification performance. FedMM notably outperforms two baselines in accuracy and AUC metrics.
  arXiv  Detail & Related papers  (2024-02-24T16:58:42Z)
• Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities [9.476402318365446]
  In this work, we introduce a novel FL architecture designed to accommodate not only the heterogeneity of data samples, but also the inherent heterogeneity/non-uniformity of data modalities across institutions. We propose a solution by devising a distributed gradient blending and proximity-aware client weighting strategy tailored for multi-modal FL.
  arXiv  Detail & Related papers  (2024-01-07T23:45:01Z)
• Multimodal Representation Learning by Alternating Unimodal Adaptation [73.15829571740866]
  We propose MLA (Multimodal Learning with Alternating Unimodal Adaptation) to overcome challenges where some modalities appear more dominant than others during multimodal learning. MLA reframes the conventional joint multimodal learning process by transforming it into an alternating unimodal learning process. It captures cross-modal interactions through a shared head, which undergoes continuous optimization across different modalities. Experiments are conducted on five diverse datasets, encompassing scenarios with complete modalities and scenarios with missing modalities.
  arXiv  Detail & Related papers  (2023-11-17T18:57:40Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• Learning Unseen Modality Interaction [54.23533023883659]
  Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. We pose the problem of unseen modality interaction and introduce a first solution. It exploits a module that projects the multidimensional features of different modalities into a common space with rich information preserved.
  arXiv  Detail & Related papers  (2023-06-22T10:53:10Z)
• Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework [89.8609061423685]
  We propose an information-theoretic approach to quantify the degree of redundancy, uniqueness, and synergy relating input modalities with an output task. To validate PID estimation, we conduct extensive experiments on both synthetic datasets where the PID is known and on large-scale multimodal benchmarks. We demonstrate their usefulness in (1) quantifying interactions within multimodal datasets, (2) quantifying interactions captured by multimodal models, (3) principled approaches for model selection, and (4) three real-world case studies.
  arXiv  Detail & Related papers  (2023-02-23T18:59:05Z)
• Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease [19.86082635340699]
  We propose a taxonomy of all reasonable ways to organize self-supervised representation-learning algorithms. We first evaluate models on toy multimodal MNIST datasets and then apply them to a multimodal neuroimaging dataset with Alzheimer's disease patients. Results show that the proposed approach outperforms previous self-supervised encoder-decoder methods.
  arXiv  Detail & Related papers  (2020-12-25T20:28:13Z)

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.