Learning Tversky Similarity

• URL: http://arxiv.org/abs/2006.11372v1
• Date: Wed, 27 May 2020 07:58:35 GMT
• Title: Learning Tversky Similarity
• Authors: Javad Rahnama and Eyke H\"ullermeier
• Abstract summary: We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods.
• Score: 3.775586678922689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods.

Related papers

• Interpretable Measures of Conceptual Similarity by Complexity-Constrained Descriptive Auto-Encoding [112.0878081944858]
  Quantifying the degree of similarity between images is a key copyright issue for image-based machine learning. We seek to define and compute a notion of "conceptual similarity" among images that captures high-level relations. Two highly dissimilar images can be discriminated early in their description, whereas conceptually dissimilar ones will need more detail to be distinguished.
  arXiv  Detail & Related papers  (2024-02-14T03:31:17Z)
• Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning [27.91000553525992]
  Self-supervised learning enables networks to learn discriminative features from massive data itself. We analyze the relation between similarity loss and feature-level cross-entropy loss. We provide theoretical analyses and experiments to show that a suitable combination of these two losses can get state-of-the-art results.
  arXiv  Detail & Related papers  (2023-01-08T13:30:39Z)
• Introspective Deep Metric Learning for Image Retrieval [80.29866561553483]
  We argue that a good similarity model should consider the semantic discrepancies with caution to better deal with ambiguous images for more robust training. We propose to represent an image using not only a semantic embedding but also an accompanying uncertainty embedding, which describes the semantic characteristics and ambiguity of an image, respectively. The proposed IDML framework improves the performance of deep metric learning through uncertainty modeling and attains state-of-the-art results on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets.
  arXiv  Detail & Related papers  (2022-05-09T17:51:44Z)
• Attributable Visual Similarity Learning [90.69718495533144]
  This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images. Motivated by the human semantic similarity cognition, we propose a generalized similarity learning paradigm to represent the similarity between two images with a graph. Experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improvements over existing deep similarity learning methods.
  arXiv  Detail & Related papers  (2022-03-28T17:35:31Z)
• Refining Self-Supervised Learning in Imaging: Beyond Linear Metric [25.96406219707398]
  We introduce in this paper a new statistical perspective, exploiting the Jaccard similarity metric, as a measure-based metric. Specifically, our proposed metric may be interpreted as a dependence measure between two adapted projections learned from the so-called latent representations. To the best of our knowledge, this effectively non-linearly fused information embedded in the Jaccard similarity, is novel to self-supervision learning with promising results.
  arXiv  Detail & Related papers  (2022-02-25T19:25:05Z)
• Learning Contrastive Representation for Semantic Correspondence [150.29135856909477]
  We propose a multi-level contrastive learning approach for semantic matching. We show that image-level contrastive learning is a key component to encourage the convolutional features to find correspondence between similar objects.
  arXiv  Detail & Related papers  (2021-09-22T18:34:14Z)
• Few-shot Visual Reasoning with Meta-analogical Contrastive Learning [141.2562447971]
  We propose to solve a few-shot (or low-shot) visual reasoning problem, by resorting to analogical reasoning. We extract structural relationships between elements in both domains, and enforce them to be as similar as possible with analogical learning. We validate our method on RAVEN dataset, on which it outperforms state-of-the-art method, with larger gains when the training data is scarce.
  arXiv  Detail & Related papers  (2020-07-23T14:00:34Z)
• Pairwise Supervision Can Provably Elicit a Decision Boundary [84.58020117487898]
  Similarity learning is a problem to elicit useful representations by predicting the relationship between a pair of patterns. We show that similarity learning is capable of solving binary classification by directly eliciting a decision boundary.
  arXiv  Detail & Related papers  (2020-06-11T05:35:16Z)

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.