Unlabeled Data Help: Minimax Analysis and Adversarial Robustness

• URL: http://arxiv.org/abs/2202.06996v1
• Date: Mon, 14 Feb 2022 19:24:43 GMT
• Title: Unlabeled Data Help: Minimax Analysis and Adversarial Robustness
• Authors: Yue Xing and Qifan Song and Guang Cheng
• Abstract summary: Self-supervised learning (SSL) approaches successfully demonstrate the great potential of supplementing learning algorithms with additional unlabeled data. It is still unclear whether the existing SSL algorithms can fully utilize the information of both labelled and unlabeled data. This paper gives an affirmative answer for the reconstruction-based SSL algorithm citeplee 2020predicting under several statistical models.
• Score: 21.79888306754263
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent proposed self-supervised learning (SSL) approaches successfully demonstrate the great potential of supplementing learning algorithms with additional unlabeled data. However, it is still unclear whether the existing SSL algorithms can fully utilize the information of both labelled and unlabeled data. This paper gives an affirmative answer for the reconstruction-based SSL algorithm \citep{lee2020predicting} under several statistical models. While existing literature only focuses on establishing the upper bound of the convergence rate, we provide a rigorous minimax analysis, and successfully justify the rate-optimality of the reconstruction-based SSL algorithm under different data generation models. Furthermore, we incorporate the reconstruction-based SSL into the existing adversarial training algorithms and show that learning from unlabeled data helps improve the robustness.

Related papers

• Can semi-supervised learning use all the data effectively? A lower bound perspective [58.71657561857055]
  We show that semi-supervised learning algorithms can leverage unlabeled data to improve over the labeled sample complexity of supervised learning algorithms. Our work suggests that, while proving performance gains for SSL algorithms is possible, it requires careful tracking of constants.
  arXiv  Detail & Related papers  (2023-11-30T13:48:50Z)
• Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency [30.94964727745347]
  We propose a method called Active Semi-supervised Learning (ASSL) to improve accuracy of models at a lower cost. ASSL involves more dynamic model updates than Active Learning (AL) due to the use of unlabeled data. ASSL achieved about 5.3 times higher computational efficiency than Semi-supervised Learning (SSL) while achieving the same performance.
  arXiv  Detail & Related papers  (2023-03-15T22:58:23Z)
• Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? [51.00034621304361]
  We study decentralized learning with unlabeled data through the lens of self-supervised learning (SSL) We study the effectiveness of contrastive learning algorithms under decentralized learning settings.
  arXiv  Detail & Related papers  (2022-10-20T01:32:41Z)
• MaxMatch: Semi-Supervised Learning with Worst-Case Consistency [149.03760479533855]
  We propose a worst-case consistency regularization technique for semi-supervised learning (SSL) We present a generalization bound for SSL consisting of the empirical loss terms observed on labeled and unlabeled training data separately. Motivated by this bound, we derive an SSL objective that minimizes the largest inconsistency between an original unlabeled sample and its multiple augmented variants.
  arXiv  Detail & Related papers  (2022-09-26T12:04:49Z)
• Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning [60.26659373318915]
  Active learning (AL) and semi-supervised learning (SSL) are two effective, but often isolated, means to alleviate the data-hungry problem. We propose an innovative Inconsistency-based virtual aDvErial algorithm to further investigate SSL-AL's potential superiority. Two real-world case studies visualize the practical industrial value of applying and deploying the proposed data sampling algorithm.
  arXiv  Detail & Related papers  (2022-06-07T13:28:43Z)
• Robust Deep Semi-Supervised Learning: A Brief Introduction [63.09703308309176]
  Semi-supervised learning (SSL) aims to improve learning performance by leveraging unlabeled data when labels are insufficient. SSL with deep models has proven to be successful on standard benchmark tasks. However, they are still vulnerable to various robustness threats in real-world applications.
  arXiv  Detail & Related papers  (2022-02-12T04:16:41Z)
• Relieving the Plateau: Active Semi-Supervised Learning for a Better Landscape [2.3046646540823916]
  Semi-supervised learning (SSL) leverages unlabeled data that are more accessible than their labeled counterparts. Active learning (AL) selects unlabeled instances to be annotated by a human-in-the-loop in hopes of better performance with less labeled data. We propose convergence rate control (CRC), an AL algorithm that selects unlabeled data to improve the problem conditioning upon inclusion to the labeled set.
  arXiv  Detail & Related papers  (2021-04-08T06:03:59Z)
• 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)
• Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning [126.31716228319902]
  We develop Distribution Aligning Refinery of Pseudo-label (DARP) algorithm. We show that DARP is provably and efficiently compatible with state-of-the-art SSL schemes.
  arXiv  Detail & Related papers  (2020-07-17T09:16:05Z)

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.