Semi-supervised Learning via Conditional Rotation Angle Estimation

• URL: http://arxiv.org/abs/2001.02865v1
• Date: Thu, 9 Jan 2020 07:06:20 GMT
• Title: Semi-supervised Learning via Conditional Rotation Angle Estimation
• Authors: Hai-Ming Xu, Lingqiao Liu, Dong Gong
• Abstract summary: We propose to couple self-supervised learning (SlfSL) with semi-supervised learning (SemSL) By implementing this idea through a simple-but-effective SlfSL approach, we create a new SemSL approach called Conditional Rotation Angle Estimation (CRAE)
• Score: 29.8660182824314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SlfSL), aiming at learning feature representations through ingeniously designed pretext tasks without human annotation, has achieved compelling progress in the past few years. Very recently, SlfSL has also been identified as a promising solution for semi-supervised learning (SemSL) since it offers a new paradigm to utilize unlabeled data. This work further explores this direction by proposing to couple SlfSL with SemSL. Our insight is that the prediction target in SemSL can be modeled as the latent factor in the predictor for the SlfSL target. Marginalizing over the latent factor naturally derives a new formulation which marries the prediction targets of these two learning processes. By implementing this idea through a simple-but-effective SlfSL approach -- rotation angle prediction, we create a new SemSL approach called Conditional Rotation Angle Estimation (CRAE). Specifically, CRAE is featured by adopting a module which predicts the image rotation angle conditioned on the candidate image class. Through experimental evaluation, we show that CRAE achieves superior performance over the other existing ways of combining SlfSL and SemSL. To further boost CRAE, we propose two extensions to strengthen the coupling between SemSL target and SlfSL target in basic CRAE. We show that this leads to an improved CRAE method which can achieve the state-of-the-art SemSL performance.

Related papers

• Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Models [0.0]
  Speech representations from large-scale ASR models contain valuable speaker information. We propose a framework to learn speaker representations in an SSL context by fine-tuning a pre-trained WavLM with a supervised loss. Our method achieves 0.99% EER on VoxCeleb1-O, establishing the new state-of-the-art on self-supervised SV.
  arXiv  Detail & Related papers  (2024-06-04T12:58:19Z)
• Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning [4.137391543972184]
  Semi-supervised learning (SSL) has witnessed remarkable progress, resulting in numerous method variations. In this paper, we present a novel SSL approach named FineSSL that significantly addresses this limitation by adapting pre-trained foundation models. We demonstrate that FineSSL sets a new state of the art for SSL on multiple benchmark datasets, reduces the training cost by over six times, and can seamlessly integrate various fine-tuning and modern SSL algorithms.
  arXiv  Detail & Related papers  (2024-05-20T03:33:12Z)
• Reinforcement Learning-Guided Semi-Supervised Learning [20.599506122857328]
  We propose a novel Reinforcement Learning Guided SSL method, RLGSSL, that formulates SSL as a one-armed bandit problem. RLGSSL incorporates a carefully designed reward function that balances the use of labeled and unlabeled data to enhance generalization performance. We demonstrate the effectiveness of RLGSSL through extensive experiments on several benchmark datasets and show that our approach achieves consistent superior performance compared to state-of-the-art SSL methods.
  arXiv  Detail & Related papers  (2024-05-02T21:52:24Z)
• Understanding and Improving the Role of Projection Head in Self-Supervised Learning [77.59320917894043]
  Self-supervised learning (SSL) aims to produce useful feature representations without access to human-labeled data annotations. Current contrastive learning approaches append a parametrized projection head to the end of some backbone network to optimize the InfoNCE objective. This raises a fundamental question: Why is a learnable projection head required if we are to discard it after training?
  arXiv  Detail & Related papers  (2022-12-22T05:42:54Z)
• Improving Self-Supervised Learning by Characterizing Idealized Representations [155.1457170539049]
  We prove necessary and sufficient conditions for any task invariant to given data augmentations. For contrastive learning, our framework prescribes simple but significant improvements to previous methods. For non-contrastive learning, we use our framework to derive a simple and novel objective.
  arXiv  Detail & Related papers  (2022-09-13T18:01:03Z)
• A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning [54.617688468341704]
  Few-shot learning aims to learn models that generalize to novel classes with limited training samples. We propose a novel paradigm containing two parts: (1) a well-designed meta-training algorithm for mitigating ambiguity between base and novel classes caused by unreliable pseudo labels and (2) a model adaptation mechanism to learn discriminative features for novel classes while preserving base knowledge using few labeled and all the unlabeled data.
  arXiv  Detail & Related papers  (2021-10-21T13:25:52Z)
• Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning [101.28281124670647]
  Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. We propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning. Our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-08-12T09:14:44Z)
• 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)
• Interventional Few-Shot Learning [88.31112565383457]
  We propose a novel Few-Shot Learning paradigm: Interventional Few-Shot Learning. Code is released at https://github.com/yue-zhongqi/ifsl.
  arXiv  Detail & Related papers  (2020-09-28T01:16:54Z)
• Semi-supervised learning objectives as log-likelihoods in a generative model of data curation [32.45282187405337]
  We formulate SSL objectives as a log-likelihood in a generative model of data curation. We give a proof-of-principle for Bayesian SSL on toy data.
  arXiv  Detail & Related papers  (2020-08-13T13:50:27Z)
• Consistency Regularization with Generative Adversarial Networks for Semi-Supervised Learning [2.9707483702447783]
  Adversarial Adversarial Networks (GANs) based semi-supervised learning (SSL) approaches are shown to improve classification performance by utilizing a large number of unlabeled samples. However, their performance still lags behind the state-of-the-art non-GAN based SSL approaches. We identify that the main reason for this is the lack of consistency in class probability predictions on the same image under local perturbations.
  arXiv  Detail & Related papers  (2020-07-08T01:47:10Z)

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.