NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning

• URL: http://arxiv.org/abs/2301.13569v2
• Date: Sun, 25 Jun 2023 15:09:48 GMT
• Title: NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning
• Authors: Jianfeng Wang, Xiaolin Hu and Thomas Lukasiewicz
• Abstract summary: Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match implicitly compares data points when making predictions, and as a result, the prediction of each unlabeled data point is affected by the labeled data points.
• Score: 86.60013228560452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match is suited to this task for two reasons. Firstly, NP-Match implicitly compares data points when making predictions, and as a result, the prediction of each unlabeled data point is affected by the labeled data points that are similar to it, which improves the quality of pseudo-labels. Secondly, NP-Match is able to estimate uncertainty that can be used as a tool for selecting unlabeled samples with reliable pseudo-labels. Compared with uncertainty-based SSL methods implemented with Monte-Carlo (MC) dropout, NP-Match estimates uncertainty with much less computational overhead, which can save time at both the training and the testing phases. We conducted extensive experiments on five public datasets under three semi-supervised image classification settings, namely, the standard semi-supervised image classification, the imbalanced semi-supervised image classification, and the multi-label semi-supervised image classification, and NP-Match outperforms state-of-the-art (SOTA) approaches or achieves competitive results on them, which shows the effectiveness of NP-Match and its potential for SSL. The codes are at https://github.com/Jianf-Wang/NP-Match

Related papers

• KD-FixMatch: Knowledge Distillation Siamese Neural Networks [13.678635878305247]
  KD-FixMatch is a novel SSL algorithm that addresses the limitations of FixMatch by incorporating knowledge distillation. The algorithm utilizes a combination of sequential and simultaneous training of SNNs to enhance performance and reduce performance degradation. Our results indicate that KD-FixMatch has a better training starting point that leads to improved model performance compared to FixMatch.
  arXiv  Detail & Related papers  (2023-09-11T21:11:48Z)
• NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation [87.50830107535533]
  Semi-supervised semantic segmentation involves assigning pixel-wise labels to unlabeled images at training time. Current approaches to semi-supervised semantic segmentation work by predicting pseudo-labels for each pixel from a class-wise probability distribution output by a model. In this work, we move one step forward by adapting NPs to semi-supervised semantic segmentation, resulting in a new model called NP-SemiSeg.
  arXiv  Detail & Related papers  (2023-08-05T12:42:15Z)
• Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks [68.36996813591425]
  We propose Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks. We enable the application of FixMatch in semi-supervised learning problems beyond image classification by adding a matching operation on the pseudo-labels. Dense FixMatch significantly improves results compared to supervised learning using only labeled data, approaching its performance with 1/4 of the labeled samples.
  arXiv  Detail & Related papers  (2022-10-18T15:02:51Z)
• NP-Match: When Neural Processes meet Semi-Supervised Learning [133.009621275051]
  Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match.
  arXiv  Detail & Related papers  (2022-07-03T15:24:31Z)
• OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers [71.08167292329028]
  We propose a novel Open-set Semi-Supervised Learning (OSSL) approach called OpenMatch. OpenMatch unifies FixMatch with novelty detection based on one-vs-all (OVA) classifiers. It achieves state-of-the-art performance on three datasets, and even outperforms a fully supervised model in detecting outliers unseen in unlabeled data on CIFAR10.
  arXiv  Detail & Related papers  (2021-05-28T23:57:15Z)
• SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification [24.386165255835063]
  A common classification task situation is where one has a large amount of data available for training, but only a small portion is with class labels. The goal of semi-supervised training, in this context, is to improve classification accuracy by leverage information from a large amount of unlabeled data. We propose a novel unsupervised objective that focuses on the less studied relationship between the high confidence unlabeled data that are similar to each other. Our proposed SimPLE algorithm shows significant performance gains over previous algorithms on CIFAR-100 and Mini-ImageNet, and is on par with the state-of-the-art methods
  arXiv  Detail & Related papers  (2021-03-30T23:48:06Z)
• Matching Distributions via Optimal Transport for Semi-Supervised Learning [31.533832244923843]
  Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data. We propose a new approach that adopts an Optimal Transport (OT) technique serving as a metric of similarity between discrete empirical probability measures. We have evaluated our proposed method with state-of-the-art SSL algorithms on standard datasets to demonstrate the superiority and effectiveness of our SSL algorithm.
  arXiv  Detail & Related papers  (2020-12-04T11:15:14Z)
• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence [93.91751021370638]
  Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling. Our algorithm, FixMatch, first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images.
  arXiv  Detail & Related papers  (2020-01-21T18:32:27Z)

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.