Similarity-Dissimilarity Loss with Supervised Contrastive Learning for Multi-label Classification

• URL: http://arxiv.org/abs/2410.13439v1
• Date: Thu, 17 Oct 2024 11:12:55 GMT
• Title: Similarity-Dissimilarity Loss with Supervised Contrastive Learning for Multi-label Classification
• Authors: Guangming Huang, Yunfei Long, Cunjin Luo, Sheng Liu,
• Abstract summary: We propose a Similarity-Dissimilarity Loss with contrastive learning for multi-label classification. Our proposed loss effectively improves the performance on all encoders under supervised contrastive learning paradigm.
• Score: 11.499489446062054
• License:
• Abstract: Supervised contrastive learning has been explored in making use of label information for multi-label classification, but determining positive samples in multi-label scenario remains challenging. Previous studies have examined strategies for identifying positive samples, considering label overlap proportion between anchors and samples. However, they ignore various relations between given anchors and samples, as well as how to dynamically adjust the weights in contrastive loss functions based on different relations, leading to great ambiguity. In this paper, we introduce five distinct relations between multi-label samples and propose a Similarity-Dissimilarity Loss with contrastive learning for multi-label classification. Our loss function re-weights the loss by computing the similarity and dissimilarity between positive samples and a given anchor based on the introduced relations. We mainly conduct experiments for multi-label text classification on MIMIC datasets, then further extend the evaluation on MS-COCO. The Experimental results show that our proposed loss effectively improves the performance on all encoders under supervised contrastive learning paradigm, demonstrating its effectiveness and robustness.

Related papers

• Multi-Label Contrastive Learning : A Comprehensive Study [48.81069245141415]
  Multi-label classification has emerged as a key area in both research and industry. Applying contrastive learning to multi-label classification presents unique challenges. We conduct an in-depth study of contrastive learning loss for multi-label classification across diverse settings.
  arXiv  Detail & Related papers  (2024-11-27T20:20:06Z)
• Multi-Label Bayesian Active Learning with Inter-Label Relationships [3.88369051454137]
  We propose a new multi-label active learning strategy to address both challenges. Our method incorporates progressively updated positive and negative correlation matrices to capture co-occurrence and disjoint relationships. Our strategy consistently achieves more reliable and superior performance, compared to several established methods.
  arXiv  Detail & Related papers  (2024-11-26T23:28:54Z)
• Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection [0.0]
  Unsupervised out-of-distribution (OOD) Detection aims to separate the samples falling outside the distribution of training data without label information. We propose Cluster-aware Contrastive Learning (CCL) framework for unsupervised OOD detection, which considers both instance-level and semantic-level information.
  arXiv  Detail & Related papers  (2023-02-06T07:21:03Z)
• Contrastive Bayesian Analysis for Deep Metric Learning [30.21464199249958]
  We develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity. This contrastive Bayesian analysis leads to a new loss function for deep metric learning. Our experimental results and ablation studies demonstrate that the proposed contrastive Bayesian metric learning method significantly improves the performance of deep metric learning.
  arXiv  Detail & Related papers  (2022-10-10T02:24:21Z)
• PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classification [64.39761523935613]
  We propose a percentile-based threshold adjusting scheme to dynamically alter the score thresholds of positive and negative pseudo-labels for each class during the training. We achieve strong performance on Pascal VOC2007 and MS-COCO datasets when compared to recent SSL methods.
  arXiv  Detail & Related papers  (2022-08-30T01:27:48Z)
• Incremental False Negative Detection for Contrastive Learning [95.68120675114878]
  We introduce a novel incremental false negative detection for self-supervised contrastive learning. During contrastive learning, we discuss two strategies to explicitly remove the detected false negatives. Our proposed method outperforms other self-supervised contrastive learning frameworks on multiple benchmarks within a limited compute.
  arXiv  Detail & Related papers  (2021-06-07T15:29:14Z)
• Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces [64.23172847182109]
  We show that different negative sampling schemes implicitly trade-off performance on dominant versus rare labels. We provide a unified means to explicitly tackle both sampling bias, arising from working with a subset of all labels, and labeling bias, which is inherent to the data due to label imbalance.
  arXiv  Detail & Related papers  (2021-05-12T15:40:13Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• Learning from Aggregate Observations [82.44304647051243]
  We study the problem of learning from aggregate observations where supervision signals are given to sets of instances. We present a general probabilistic framework that accommodates a variety of aggregate observations. Simple maximum likelihood solutions can be applied to various differentiable models.
  arXiv  Detail & Related papers  (2020-04-14T06:18:50Z)

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.