Multi-view Feature Extraction based on Triple Contrastive Heads

• URL: http://arxiv.org/abs/2303.12615v1
• Date: Wed, 22 Mar 2023 14:56:51 GMT
• Title: Multi-view Feature Extraction based on Triple Contrastive Heads
• Authors: Hongjie Zhang
• Abstract summary: We propose a novel multi-view feature extraction method based on triple contrastive heads. The proposed method offers a strong advantage for multi-view feature extraction.
• Score: 1.4467794332678539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view feature extraction is an efficient approach for alleviating the issue of dimensionality in highdimensional multi-view data. Contrastive learning (CL), which is a popular self-supervised learning method, has recently attracted considerable attention. In this study, we propose a novel multi-view feature extraction method based on triple contrastive heads, which combines the sample-, recovery- , and feature-level contrastive losses to extract the sufficient yet minimal subspace discriminative information in compliance with information bottleneck principle. In MFETCH, we construct the feature-level contrastive loss, which removes the redundent information in the consistency information to achieve the minimality of the subspace discriminative information. Moreover, the recovery-level contrastive loss is also constructed in MFETCH, which captures the view-specific discriminative information to achieve the sufficiency of the subspace discriminative information.The numerical experiments demonstrate that the proposed method offers a strong advantage for multi-view feature extraction.

Related papers

• Multimodal Information Bottleneck for Deep Reinforcement Learning with Multiple Sensors [10.454194186065195]
  Reinforcement learning has achieved promising results on robotic control tasks but struggles to leverage information effectively. Recent works construct auxiliary losses based on reconstruction or mutual information to extract joint representations from multiple sensory inputs. We argue that compressing information in the learned joint representations about raw multimodal observations is helpful.
  arXiv  Detail & Related papers  (2024-10-23T04:32:37Z)
• 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)
• Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate Metric [99.19559537966538]
  DML aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. To maintain the structure of embedding space and avoid feature collapse, we propose a novel loss function called Anti-Collapse Loss. Comprehensive experiments on benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-03T13:44:20Z)
• An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition [49.45660055499103]
  Zero-shot human skeleton-based action recognition aims to construct a model that can recognize actions outside the categories seen during training. Previous research has focused on aligning sequences' visual and semantic spatial distributions. We introduce a new loss function sampling method to obtain a tight and robust representation.
  arXiv  Detail & Related papers  (2024-06-02T06:53:01Z)
• Differentiable Information Bottleneck for Deterministic Multi-view Clustering [9.723389925212567]
  We propose a new differentiable information bottleneck (DIB) method, which provides a deterministic and analytical MVC solution. Specifically, we first propose to directly fit the mutual information of high-dimensional spaces by leveraging normalized kernel Gram matrix. Then, based on the new mutual information measurement, a deterministic multi-view neural network with analytical gradients is explicitly trained to parameterize IB principle.
  arXiv  Detail & Related papers  (2024-03-23T02:13:22Z)
• Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition [57.08108545219043]
  Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing local-based representation approaches. This article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition.
  arXiv  Detail & Related papers  (2023-05-12T00:13:17Z)
• Preventing Dimensional Collapse of Incomplete Multi-View Clustering via Direct Contrastive Learning [0.14999444543328289]
  We propose a novel incomplete multi-view contrastive clustering framework. It effectively avoids dimensional collapse without relying on projection heads. It achieves state-of-the-art clustering results on 5 public datasets.
  arXiv  Detail & Related papers  (2023-03-22T00:21:50Z)
• Multi-view Feature Extraction based on Dual Contrastive Head [1.4467794332678539]
  We propose a novel multiview feature extraction method based on dual contrastive head. It introduces structural-level contrastive loss into sample-level CL-based method. Numerical experiments on six real datasets demonstrate the superior performance of the proposed method.
  arXiv  Detail & Related papers  (2023-02-08T08:13:17Z)
• Federated Representation Learning via Maximal Coding Rate Reduction [109.26332878050374]
  We propose a methodology to learn low-dimensional representations from a dataset that is distributed among several clients. Our proposed method, which we refer to as FLOW, utilizes MCR2 as the objective of choice, hence resulting in representations that are both between-class discriminative and within-class compressible.
  arXiv  Detail & Related papers  (2022-10-01T15:43:51Z)
• Variational Distillation for Multi-View Learning [104.17551354374821]
  We design several variational information bottlenecks to exploit two key characteristics for multi-view representation learning. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels.
  arXiv  Detail & Related papers  (2022-06-20T03:09:46Z)

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.