Pairwise Similarity Learning is SimPLE

• URL: http://arxiv.org/abs/2310.09449v1
• Date: Fri, 13 Oct 2023 23:56:47 GMT
• Title: Pairwise Similarity Learning is SimPLE
• Authors: Yandong Wen, Weiyang Liu, Yao Feng, Bhiksha Raj, Rita Singh, Adrian Weller, Michael J. Black, Bernhard Sch\"olkopf
• Abstract summary: We focus on a general yet important learning problem, pairwise similarity learning (PSL) PSL subsumes a wide range of important applications, such as open-set face recognition, speaker verification, image retrieval and person re-identification. We propose a surprisingly simple proxy-free method, called SimPLE, which requires neither feature/proxy normalization nor angular margin.
• Score: 104.14303849615496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL). PSL subsumes a wide range of important applications, such as open-set face recognition, speaker verification, image retrieval and person re-identification. The goal of PSL is to learn a pairwise similarity function assigning a higher similarity score to positive pairs (i.e., a pair of samples with the same label) than to negative pairs (i.e., a pair of samples with different label). We start by identifying a key desideratum for PSL, and then discuss how existing methods can achieve this desideratum. We then propose a surprisingly simple proxy-free method, called SimPLE, which requires neither feature/proxy normalization nor angular margin and yet is able to generalize well in open-set recognition. We apply the proposed method to three challenging PSL tasks: open-set face recognition, image retrieval and speaker verification. Comprehensive experimental results on large-scale benchmarks show that our method performs significantly better than current state-of-the-art methods.

Related papers

• MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments [72.6405488990753]
  Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks. We propose a single-stage and standalone method, MOCA, which unifies both desired properties. We achieve new state-of-the-art results on low-shot settings and strong experimental results in various evaluation protocols.
  arXiv  Detail & Related papers  (2023-07-18T15:46:20Z)
• Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods [4.680881326162484]
  Self-supervised learning algorithms (SSL) based on instance discrimination have shown promising results. We propose an approach to identify those images with similar semantic content and treat them as positive instances. We run experiments on three benchmark datasets: ImageNet, STL-10 and CIFAR-10 with different instance discrimination SSL approaches.
  arXiv  Detail & Related papers  (2023-06-28T11:47:08Z)
• Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning [35.53729744330751]
  Contrastive learning methods train visual encoders by comparing views from one instance to others. This binary instance discrimination is studied extensively to improve feature representations in self-supervised learning. In this paper, we rethink the instance discrimination framework and find the binary instance labeling insufficient to measure correlations between different samples.
  arXiv  Detail & Related papers  (2023-03-30T04:22:07Z)
• Semantic Cross Attention for Few-shot Learning [9.529264466445236]
  We propose a multi-task learning approach to view semantic features of label text as an auxiliary task. Our proposed model uses word-embedding representations as semantic features to help train the embedding network and a semantic cross-attention module to bridge the semantic features into the typical visual modal.
  arXiv  Detail & Related papers  (2022-10-12T15:24:59Z)
• Non-contrastive representation learning for intervals from well logs [58.70164460091879]
  The representation learning problem in the oil & gas industry aims to construct a model that provides a representation based on logging data for a well interval. One of the possible approaches is self-supervised learning (SSL) We are the first to introduce non-contrastive SSL for well-logging data.
  arXiv  Detail & Related papers  (2022-09-28T13:27:10Z)
• 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)
• ReSSL: Relational Self-Supervised Learning with Weak Augmentation [68.47096022526927]
  Self-supervised learning has achieved great success in learning visual representations without data annotations. We introduce a novel relational SSL paradigm that learns representations by modeling the relationship between different instances. Our proposed ReSSL significantly outperforms the previous state-of-the-art algorithms in terms of both performance and training efficiency.
  arXiv  Detail & Related papers  (2021-07-20T06:53:07Z)
• Whitening for Self-Supervised Representation Learning [129.57407186848917]
  We propose a new loss function for self-supervised representation learning (SSL) based on the whitening of latent-space features. Our solution does not require asymmetric networks and it is conceptually simple.
  arXiv  Detail & Related papers  (2020-07-13T12:33:25Z)

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.