Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization

• URL: http://arxiv.org/abs/2210.01908v3
• Date: Fri, 2 Jun 2023 15:25:04 GMT
• Title: Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
• Authors: Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
• Abstract summary: Many metric learning loss functions focus on learning a correct ranking of training samples, but strongly overfit semantically inconsistent labels. We propose a new metric learning method, called contextual loss, which optimize contextual similarity in addition to cosine similarity. We empirically show that the proposed loss is more robust to label noise, and is less prone to overfitting even when a large portion of train data is withheld.
• Score: 16.14184145802016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is extensive interest in metric learning methods for image retrieval. Many metric learning loss functions focus on learning a correct ranking of training samples, but strongly overfit semantically inconsistent labels and require a large amount of data. To address these shortcomings, we propose a new metric learning method, called contextual loss, which optimizes contextual similarity in addition to cosine similarity. Our contextual loss implicitly enforces semantic consistency among neighbors while converging to the correct ranking. We empirically show that the proposed loss is more robust to label noise, and is less prone to overfitting even when a large portion of train data is withheld. Extensive experiments demonstrate that our method achieves a new state-of-the-art across four image retrieval benchmarks and multiple different evaluation settings. Code is available at: https://github.com/Chris210634/metric-learning-using-contextual-similarity

Related papers

• Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning [100.7407460674153]
  Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks. To mitigate the problem, a line of methods propose to replay the data of experienced tasks when learning new tasks. However, it is not expected in practice considering the memory constraint or data privacy issue. As a replacement, data-free data replay methods are proposed by inverting samples from the classification model.
  arXiv  Detail & Related papers  (2024-01-12T12:51:12Z)
• SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification [24.386165255835063]
  A common classification task situation is where one has a large amount of data available for training, but only a small portion is with class labels. The goal of semi-supervised training, in this context, is to improve classification accuracy by leverage information from a large amount of unlabeled data. We propose a novel unsupervised objective that focuses on the less studied relationship between the high confidence unlabeled data that are similar to each other. Our proposed SimPLE algorithm shows significant performance gains over previous algorithms on CIFAR-100 and Mini-ImageNet, and is on par with the state-of-the-art methods
  arXiv  Detail & Related papers  (2021-03-30T23:48:06Z)
• Grafit: Learning fine-grained image representations with coarse labels [114.17782143848315]
  This paper tackles the problem of learning a finer representation than the one provided by training labels. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods.
  arXiv  Detail & Related papers  (2020-11-25T19:06:26Z)
• Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection [69.2370349274216]
  Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Semantic components are distilled from utterances via multi-head self-attention. Our method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.
  arXiv  Detail & Related papers  (2020-10-06T05:16:38Z)
• Adaptive additive classification-based loss for deep metric learning [0.0]
  We propose an extension to the existing adaptive margin for classification-based deep metric learning. Our results were achieved with faster convergence and lower code complexity than the prior state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-25T20:45:22Z)
• Towards Certified Robustness of Distance Metric Learning [53.96113074344632]
  We advocate imposing an adversarial margin in the input space so as to improve the generalization and robustness of metric learning algorithms. We show that the enlarged margin is beneficial to the generalization ability by using the theoretical technique of algorithmic robustness.
  arXiv  Detail & Related papers  (2020-06-10T16:51:53Z)
• Rethinking preventing class-collapsing in metric learning with margin-based losses [81.22825616879936]
  Metric learning seeks embeddings where visually similar instances are close and dissimilar instances are apart. margin-based losses tend to project all samples of a class onto a single point in the embedding space. We propose a simple modification to the embedding losses such that each sample selects its nearest same-class counterpart in a batch.
  arXiv  Detail & Related papers  (2020-06-09T09:59:25Z)
• Sharing Matters for Generalization in Deep Metric Learning [22.243744691711452]
  This work investigates how to learn characteristics that separate between classes without the need for annotations or training data. By formulating our approach as a novel triplet sampling strategy, it can be easily applied on top of recent ranking loss frameworks.
  arXiv  Detail & Related papers  (2020-04-12T10:21:15Z)
• Learning to Compare Relation: Semantic Alignment for Few-Shot Learning [48.463122399494175]
  We present a novel semantic alignment model to compare relations, which is robust to content misalignment. We conduct extensive experiments on several few-shot learning datasets.
  arXiv  Detail & Related papers  (2020-02-29T08:37:02Z)
• Circle Loss: A Unified Perspective of Pair Similarity Optimization [42.33948436767691]
  We find a majority of loss functions, including the triplet loss and the softmax plus cross-entropy loss. We show that the Circle loss offers a more flexible optimization approach towards a more definite convergence target.
  arXiv  Detail & Related papers  (2020-02-25T13:56:40Z)

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.