Pruning the Unlabeled Data to Improve Semi-Supervised Learning

• URL: http://arxiv.org/abs/2308.14058v1
• Date: Sun, 27 Aug 2023 09:45:41 GMT
• Title: Pruning the Unlabeled Data to Improve Semi-Supervised Learning
• Authors: Guy Hacohen, Daphna Weinshall
• Abstract summary: We present PruneSSL, a technique for selectively removing examples from the original unlabeled dataset to enhance its separability. Although PruneSSL reduces the quantity of available training data for the learner, it significantly improves the performance of various competitive SSL algorithms.
• Score: 17.62242617965356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the domain of semi-supervised learning (SSL), the conventional approach involves training a learner with a limited amount of labeled data alongside a substantial volume of unlabeled data, both drawn from the same underlying distribution. However, for deep learning models, this standard practice may not yield optimal results. In this research, we propose an alternative perspective, suggesting that distributions that are more readily separable could offer superior benefits to the learner as compared to the original distribution. To achieve this, we present PruneSSL, a practical technique for selectively removing examples from the original unlabeled dataset to enhance its separability. We present an empirical study, showing that although PruneSSL reduces the quantity of available training data for the learner, it significantly improves the performance of various competitive SSL algorithms, thereby achieving state-of-the-art results across several image classification tasks.

Related papers

• Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition [50.61991746981703]
  Current state-of-the-art LTSSL approaches rely on high-quality pseudo-labels for large-scale unlabeled data. This paper introduces a novel probabilistic framework that unifies various recent proposals in long-tail learning. We introduce a continuous contrastive learning method, CCL, extending our framework to unlabeled data using reliable and smoothed pseudo-labels.
  arXiv  Detail & Related papers  (2024-10-08T15:06:10Z)
• ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation [24.743048965822297]
  This paper introduces a novel semi-supervised LiDAR semantic segmentation framework called ItTakesTwo (IT2) IT2 is designed to ensure consistent predictions from peer LiDAR representations, thereby improving the perturbation effectiveness in consistency learning. Results on public benchmarks show that our approach achieves remarkable improvements over the previous state-of-the-art (SOTA) methods in the field.
  arXiv  Detail & Related papers  (2024-07-09T18:26:53Z)
• A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification [61.473485511491795]
  Semi-supervised learning (SSL) is a practical challenge in computer vision. Pseudo-label (PL) methods, e.g., FixMatch and FreeMatch, obtain the State Of The Art (SOTA) performances in SSL. We propose a lightweight channel-based ensemble method to consolidate multiple inferior PLs into the theoretically guaranteed unbiased and low-variance one.
  arXiv  Detail & Related papers  (2024-03-27T09:49:37Z)
• On Pretraining Data Diversity for Self-Supervised Learning [57.91495006862553]
  We explore the impact of training with more diverse datasets on the performance of self-supervised learning (SSL) under a fixed computational budget. Our findings consistently demonstrate that increasing pretraining data diversity enhances SSL performance, albeit only when the distribution distance to the downstream data is minimal.
  arXiv  Detail & Related papers  (2024-03-20T17:59:58Z)
• Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning [76.00798972439004]
  Collaborative Sample Selection (CSS) removes noisy samples from identified clean set. We introduce a co-training mechanism with a contrastive loss in semi-supervised learning.
  arXiv  Detail & Related papers  (2023-10-24T05:37:20Z)
• Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models [39.42802115580677]
  Semi-supervised learning (SSL) can leverage both labeled and unlabeled data to build a predictive model. Recent literature suggests that naively applying state-of-the-art SSL with a pretrained model fails to unleash the full potential of training data. We propose to use pseudo-labels from the unlabelled data to update the feature extractor that is less sensitive to incorrect labels.
  arXiv  Detail & Related papers  (2023-09-09T01:57:14Z)
• 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)
• Deep Stable Learning for Out-Of-Distribution Generalization [27.437046504902938]
  Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution. Eliminating the impact of distribution shifts between training and testing data is crucial for building performance-promising deep models. We propose to address this problem by removing the dependencies between features via learning weights for training samples.
  arXiv  Detail & Related papers  (2021-04-16T03:54:21Z)
• A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification [38.68079253627819]
  Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.
  arXiv  Detail & Related papers  (2021-04-01T17:59:41Z)
• On Data-Augmentation and Consistency-Based Semi-Supervised Learning [77.57285768500225]
  Recently proposed consistency-based Semi-Supervised Learning (SSL) methods have advanced the state of the art in several SSL tasks. Despite these advances, the understanding of these methods is still relatively limited.
  arXiv  Detail & Related papers  (2021-01-18T10:12:31Z)
• 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)

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.