DAS: Densely-Anchored Sampling for Deep Metric Learning

• URL: http://arxiv.org/abs/2208.00119v1
• Date: Sat, 30 Jul 2022 02:07:46 GMT
• Title: DAS: Densely-Anchored Sampling for Deep Metric Learning
• Authors: Lizhao Liu, Shangxin Huang, Zhuangwei Zhuang, Ran Yang, Mingkui Tan, Yaowei Wang
• Abstract summary: We propose a Densely-Anchored Sampling (DAS) scheme that exploits the anchor's nearby embedding space to densely produce embeddings without data points. Our method is effortlessly integrated into existing DML frameworks and improves them without bells and whistles.
• Score: 43.81322638018864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Metric Learning (DML) serves to learn an embedding function to project semantically similar data into nearby embedding space and plays a vital role in many applications, such as image retrieval and face recognition. However, the performance of DML methods often highly depends on sampling methods to choose effective data from the embedding space in the training. In practice, the embeddings in the embedding space are obtained by some deep models, where the embedding space is often with barren area due to the absence of training points, resulting in so called "missing embedding" issue. This issue may impair the sample quality, which leads to degenerated DML performance. In this work, we investigate how to alleviate the "missing embedding" issue to improve the sampling quality and achieve effective DML. To this end, we propose a Densely-Anchored Sampling (DAS) scheme that considers the embedding with corresponding data point as "anchor" and exploits the anchor's nearby embedding space to densely produce embeddings without data points. Specifically, we propose to exploit the embedding space around single anchor with Discriminative Feature Scaling (DFS) and multiple anchors with Memorized Transformation Shifting (MTS). In this way, by combing the embeddings with and without data points, we are able to provide more embeddings to facilitate the sampling process thus boosting the performance of DML. Our method is effortlessly integrated into existing DML frameworks and improves them without bells and whistles. Extensive experiments on three benchmark datasets demonstrate the superiority of our method.

Related papers

• Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection [84.78475642696137]
  The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. We propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS) SGPS constructs reliable positive pairs for noisy samples to enhance the sample utilization.
  arXiv  Detail & Related papers  (2025-01-19T14:41:55Z)
• An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion for Long-tailed Multispectral Point Clouds [67.96583737413296]
  We propose an enhanced classification method based on adaptive multi-scale fusion for MPCs with long-tailed distributions. In the training set generation stage, a grid-balanced sampling strategy is designed to reliably generate training samples from sparse labeled datasets. In the feature learning stage, a multi-scale feature fusion module is proposed to fuse shallow features of land-covers at different scales.
  arXiv  Detail & Related papers  (2024-12-16T03:21:20Z)
• Weakly Contrastive Learning via Batch Instance Discrimination and Feature Clustering for Small Sample SAR ATR [7.2932563202952725]
  We propose a novel framework named Batch Instance Discrimination and Feature Clustering (BIDFC) In this framework, embedding distance between samples should be moderate because of the high similarity between samples in the SAR images. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database indicate a 91.25% classification accuracy of our method fine-tuned on only 3.13% training data.
  arXiv  Detail & Related papers  (2024-08-07T08:39:33Z)
• Downstream-Pretext Domain Knowledge Traceback for Active Learning [138.02530777915362]
  We propose a downstream-pretext domain knowledge traceback (DOKT) method that traces the data interactions of downstream knowledge and pre-training guidance. DOKT consists of a traceback diversity indicator and a domain-based uncertainty estimator. Experiments conducted on ten datasets show that our model outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-20T01:34:13Z)
• TraceMesh: Scalable and Streaming Sampling for Distributed Traces [51.08892669409318]
  TraceMesh is a scalable and streaming sampler for distributed traces. It accommodates previously unseen trace features in a unified and streamlined way. TraceMesh outperforms state-of-the-art methods by a significant margin in both sampling accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-06-11T06:13:58Z)
• Anchor-aware Deep Metric Learning for Audio-visual Retrieval [11.675472891647255]
  Metric learning aims at capturing the underlying data structure and enhancing the performance of tasks like audio-visual cross-modal retrieval (AV-CMR) Recent works employ sampling methods to select impactful data points from the embedding space during training. However, the model training fails to fully explore the space due to the scarcity of training data points. We propose an innovative Anchor-aware Deep Metric Learning (AADML) method to address this challenge.
  arXiv  Detail & Related papers  (2024-04-21T22:44:44Z)
• DLME: Deep Local-flatness Manifold Embedding [41.86924171938867]
  Deep Local-flatness Manifold Embedding (DLME) is a novel ML framework to obtain reliable manifold embedding by reducing distortion. In the experiments, by showing the effectiveness of DLME on downstream classification, clustering, and visualization tasks, our results show that DLME outperforms SOTA ML & contrastive learning (CL) methods.
  arXiv  Detail & Related papers  (2022-07-07T08:46:17Z)
• Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding [62.17020485045456]
  It is commonly assumed in semi-supervised learning (SSL) that the unlabeled data are drawn from the same distribution as that of the labeled ones. We propose to selectively utilize unlabeled data through sample weighting, so that only conducive unlabeled data would be prioritized.
  arXiv  Detail & Related papers  (2022-05-02T16:09:17Z)
• Multimodal-Aware Weakly Supervised Metric Learning with Self-weighting Triplet Loss [2.010312620798609]
  We propose a novel weakly supervised metric learning algorithm, named MultimoDal Aware weakly supervised Metric Learning (MDaML) MDaML partitions the data space into several clusters and allocates the local cluster centers and weight for each sample. Experiments conducted on 13 datasets validate the superiority of the proposed MDaML.
  arXiv  Detail & Related papers  (2021-02-03T07:27:05Z)

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.