Positive Unlabeled Contrastive Learning

• URL: http://arxiv.org/abs/2206.01206v3
• Date: Thu, 28 Mar 2024 23:25:14 GMT
• Title: Positive Unlabeled Contrastive Learning
• Authors: Anish Acharya, Sujay Sanghavi, Li Jing, Bhargav Bhushanam, Dhruv Choudhary, Michael Rabbat, Inderjit Dhillon,
• Abstract summary: We extend the self-supervised pretraining paradigm to the classical positive unlabeled (PU) setting. We develop a simple methodology to pseudo-label the unlabeled samples using a new PU-specific clustering scheme. Our method handily outperforms state-of-the-art PU methods over several standard PU benchmark datasets.
• Score: 14.975173394072053
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Self-supervised pretraining on unlabeled data followed by supervised fine-tuning on labeled data is a popular paradigm for learning from limited labeled examples. We extend this paradigm to the classical positive unlabeled (PU) setting, where the task is to learn a binary classifier given only a few labeled positive samples, and (often) a large amount of unlabeled samples (which could be positive or negative). We first propose a simple extension of standard infoNCE family of contrastive losses, to the PU setting; and show that this learns superior representations, as compared to existing unsupervised and supervised approaches. We then develop a simple methodology to pseudo-label the unlabeled samples using a new PU-specific clustering scheme; these pseudo-labels can then be used to train the final (positive vs. negative) classifier. Our method handily outperforms state-of-the-art PU methods over several standard PU benchmark datasets, while not requiring a-priori knowledge of any class prior (which is a common assumption in other PU methods). We also provide a simple theoretical analysis that motivates our methods.

Related papers

• Contrastive Approach to Prior Free Positive Unlabeled Learning [15.269090018352875]
  We propose a novel PU learning framework, that starts by learning a feature space through pretext-invariant representation learning. Our proposed approach handily outperforms state-of-the-art PU learning methods across several standard PU benchmark datasets.
  arXiv  Detail & Related papers  (2024-02-08T20:20:54Z)
• Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning [55.4510979153023]
  In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. To help these mislabeled samples "appeal," we propose the first appeal-based framework.
  arXiv  Detail & Related papers  (2023-12-18T09:09:52Z)
• Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction [48.929877651182885]
  Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature. We propose a new robust PU learning method with a training strategy motivated by the nature of human learning.
  arXiv  Detail & Related papers  (2023-08-01T04:34:52Z)
• Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning [97.88458953075205]
  Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. This paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner.
  arXiv  Detail & Related papers  (2023-05-04T12:52:18Z)
• Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective [89.5370481649529]
  We propose a label distribution perspective for PU learning in this paper. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions. Experiments on three benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-12-06T07:38:29Z)
• Learning with Proper Partial Labels [87.65718705642819]
  Partial-label learning is a kind of weakly-supervised learning with inexact labels. We show that this proper partial-label learning framework includes many previous partial-label learning settings. We then derive a unified unbiased estimator of the classification risk.
  arXiv  Detail & Related papers  (2021-12-23T01:37:03Z)
• Adaptive Positive-Unlabelled Learning via Markov Diffusion [0.0]
  Positive-Unlabelled (PU) learning is the machine learning setting in which only a set of positive instances are labelled. The principal aim of the algorithm is to identify a set of instances which are likely to contain positive instances that were originally unlabelled.
  arXiv  Detail & Related papers  (2021-08-13T10:25:47Z)
• Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation [85.75352990739154]
  We propose a novel PU classification method based on density ratio estimation. A notable advantage of our proposed method is that it does not require the class-priors in the training phase.
  arXiv  Detail & Related papers  (2021-07-11T13:36:53Z)

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.