Similarity Metric Learning Task

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A Similarity Metric Learning Task is a machine learning task where the goal is to learn a similarity scoring model (tha scores the similarity between two or more objects).



References

2021

  • (Liu et al., 2021) ⇒ Xiaoqian Liu, Xiaoyu Tang, and Shuying Chen. (2021). “Learning a Similarity Metric Discriminatively with Application to Ancient Character Recognition." In: 14th International Conference, KSEM 2021, Tokyo, Japan, September 13–15, 2021, Proceedings. Springer.
    • QUOTE: "The process of learning good representation in deep learning may prove difficult when the data is insufficient. In this paper, we propose a Siamese similarity network for one-shot ..."
    • NOTE: It explores similarity learning for recognizing ancient characters using limited data.

2011

  • (Wang et al., 2011) ⇒ Tinghuai Wang, Shengjin Wang, and Xinyu Ding. (2011). “Learning a Similarity Metric Discriminatively for Pose Exemplar Based Action Recognition." In: 2011 4th International Congress on Image and Signal Processing. IEEE.
    • QUOTE: "Exemplar-based action recognition has the advantages of being compact and time-invariant. But how to select suitable exemplars and measure the pose similarities between frames ..."
    • NOTE: It applies similarity learning techniques for exemplar-based action recognition.

2010

2005

  • (Chopra et al., 2005) ⇒ Sumit Chopra, Raia Hadsell, and Yann LeCun. (2005). “Learning a Similarity Metric Discriminatively, with Application to Face Verification". In: CVPR (1). pp. 539-546.
    • QUOTE: "In this paper we address the task of learning a similarity function from data. ... Our experiments demonstrate that the proposed techniques can learn a similarity metric ..."
    • NOTE: It introduces techniques for similarity learning focusing on applications to face verification.

1995

  • (Lowe, 1995) ⇒ David G. Lowe. (1995). “Similarity Metric Learning for a Variable-Kernel Classifier." In: Neural Computation.
    • QUOTE: "Nearest-neighbor interpolation algorithms have many useful properties for applications to learning, but they often exhibit poor generalization. In this paper, it is shown that much better ..."
    • NOTE: It examines similarity metric learning to improve nearest-neighbor based classification.