ProcSim: Proxy-based Confidence for Robust Similarity Learning

• URL: http://arxiv.org/abs/2311.00668v1
• Date: Wed, 1 Nov 2023 17:17:14 GMT
• Title: ProcSim: Proxy-based Confidence for Robust Similarity Learning
• Authors: Oriol Barbany, Xiaofan Lin, Muhammet Bastan, Arnab Dhua
• Abstract summary: We show that popular benchmark datasets often contain numerous wrong labels, and DML methods are susceptible to them. Intending to study the effect of realistic noise, we create an ontology of the classes in a dataset and use it to simulate semantically coherent labeling mistakes. To train robust DML models, we propose ProcSim, a framework that assigns a confidence score to each sample using the normalized distance to its class representative.
• Score: 0.6963971634605796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Metric Learning (DML) methods aim at learning an embedding space in which distances are closely related to the inherent semantic similarity of the inputs. Previous studies have shown that popular benchmark datasets often contain numerous wrong labels, and DML methods are susceptible to them. Intending to study the effect of realistic noise, we create an ontology of the classes in a dataset and use it to simulate semantically coherent labeling mistakes. To train robust DML models, we propose ProcSim, a simple framework that assigns a confidence score to each sample using the normalized distance to its class representative. The experimental results show that the proposed method achieves state-of-the-art performance on the DML benchmark datasets injected with uniform and the proposed semantically coherent noise.

Related papers

• 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)
• Relation Modeling and Distillation for Learning with Noisy Labels [4.556974104115929]
  This paper proposes a relation modeling and distillation framework that models inter-sample relationships via self-supervised learning. The proposed framework can learn discriminative representations for noisy data, which results in superior performance than the existing methods.
  arXiv  Detail & Related papers  (2024-05-30T01:47:27Z)
• 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)
• Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
  Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
  arXiv  Detail & Related papers  (2024-01-18T08:12:23Z)
• Variational Self-Supervised Contrastive Learning Using Beta Divergence [0.0]
  We present a contrastive self-supervised learning method which is robust to data noise, grounded in the domain of variational methods. We demonstrate the effectiveness of the proposed method through rigorous experiments including linear evaluation and fine-tuning scenarios with multi-label datasets in the face understanding domain.
  arXiv  Detail & Related papers  (2023-09-05T17:21:38Z)
• Meta Objective Guided Disambiguation for Partial Label Learning [44.05801303440139]
  We propose a novel framework for partial label learning with meta objective guided disambiguation (MoGD) MoGD aims to recover the ground-truth label from candidate labels set by solving a meta objective on a small validation set. The proposed method can be easily implemented by using various deep networks with the ordinary SGD.
  arXiv  Detail & Related papers  (2022-08-26T06:48:01Z)
• Adaptive neighborhood Metric learning [184.95321334661898]
  We propose a novel distance metric learning algorithm, named adaptive neighborhood metric learning (ANML) ANML can be used to learn both the linear and deep embeddings. The emphlog-exp mean function proposed in our method gives a new perspective to review the deep metric learning methods.
  arXiv  Detail & Related papers  (2022-01-20T17:26:37Z)
• Adaptive Hierarchical Similarity Metric Learning with Noisy Labels [138.41576366096137]
  We propose an Adaptive Hierarchical Similarity Metric Learning method. It considers two noise-insensitive information, textiti.e., class-wise divergence and sample-wise consistency. Our method achieves state-of-the-art performance compared with current deep metric learning approaches.
  arXiv  Detail & Related papers  (2021-10-29T02:12:18Z)
• MAML is a Noisy Contrastive Learner [72.04430033118426]
  Model-agnostic meta-learning (MAML) is one of the most popular and widely-adopted meta-learning algorithms nowadays. We provide a new perspective to the working mechanism of MAML and discover that: MAML is analogous to a meta-learner using a supervised contrastive objective function. We propose a simple but effective technique, zeroing trick, to alleviate such interference.
  arXiv  Detail & Related papers  (2021-06-29T12:52:26Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

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.