Probabilistic Test-Time Generalization by Variational Neighbor-Labeling

• URL: http://arxiv.org/abs/2307.04033v3
• Date: Mon, 1 Jul 2024 12:46:35 GMT
• Title: Probabilistic Test-Time Generalization by Variational Neighbor-Labeling
• Authors: Sameer Ambekar, Zehao Xiao, Jiayi Shen, Xiantong Zhen, Cees G. M. Snoek,
• Abstract summary: This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed on unseen target domains. Probability pseudo-labeling of target samples to generalize the source-trained model to the target domain at test time. Variational neighbor labels that incorporate the information of neighboring target samples to generate more robust pseudo labels.
• Score: 62.158807685159736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed on unseen target domains. We follow the strict separation of source training and target testing, but exploit the value of the unlabeled target data itself during inference. We make three contributions. First, we propose probabilistic pseudo-labeling of target samples to generalize the source-trained model to the target domain at test time. We formulate the generalization at test time as a variational inference problem, by modeling pseudo labels as distributions, to consider the uncertainty during generalization and alleviate the misleading signal of inaccurate pseudo labels. Second, we learn variational neighbor labels that incorporate the information of neighboring target samples to generate more robust pseudo labels. Third, to learn the ability to incorporate more representative target information and generate more precise and robust variational neighbor labels, we introduce a meta-generalization stage during training to simulate the generalization procedure. Experiments on seven widely-used datasets demonstrate the benefits, abilities, and effectiveness of our proposal.

Related papers

• Overcoming label shift in targeted federated learning [8.223143536605248]
  Federated learning enables multiple actors to collaboratively train models without sharing private data. One common violation is label shift, where the label distributions differ across clients or between clients and the target domain. We propose FedPALS, a novel model aggregation scheme that adapts to label shifts by leveraging knowledge of the target label distribution at the central server.
  arXiv  Detail & Related papers  (2024-11-06T09:52:45Z)
• Memory Consistency Guided Divide-and-Conquer Learning for Generalized Category Discovery [56.172872410834664]
  Generalized category discovery (GCD) aims at addressing a more realistic and challenging setting of semi-supervised learning. We propose a Memory Consistency guided Divide-and-conquer Learning framework (MCDL) Our method outperforms state-of-the-art models by a large margin on both seen and unseen classes of the generic image recognition.
  arXiv  Detail & Related papers  (2024-01-24T09:39:45Z)
• Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias [5.698050337128548]
  Self-training is a well-known approach for semi-supervised learning. It consists of iteratively assigning pseudo-labels to unlabeled data for which the model is confident and treating them as labeled examples. For neural networks, softmax prediction probabilities are often used as a confidence measure, although they are known to be overconfident, even for wrong predictions. We propose a novel confidence measure, called $mathcalT$-similarity, built upon the prediction diversity of an ensemble of linear classifiers.
  arXiv  Detail & Related papers  (2023-10-23T11:30:06Z)
• Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning [10.396596055773012]
  We propose a novel complementary learning approach to enhance test-time adaptation. In test-time adaptation tasks, information from the source domain is typically unavailable. We highlight that the risk function of complementary labels agrees with their Vanilla loss formula.
  arXiv  Detail & Related papers  (2023-01-15T03:36:33Z)
• Combating Label Distribution Shift for Active Domain Adaptation [16.270897459117755]
  We consider the problem of active domain adaptation (ADA) to unlabeled target data. Inspired by recent analysis on a critical issue from label distribution mismatch between source and target in domain adaptation, we devise a method that addresses the issue for the first time in ADA.
  arXiv  Detail & Related papers  (2022-08-13T09:06:45Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Out-distribution aware Self-training in an Open World Setting [62.19882458285749]
  We leverage unlabeled data in an open world setting to further improve prediction performance. We introduce out-distribution aware self-training, which includes a careful sample selection strategy. Our classifiers are by design out-distribution aware and can thus distinguish task-related inputs from unrelated ones.
  arXiv  Detail & Related papers  (2020-12-21T12:25:04Z)
• MatchGAN: A Self-Supervised Semi-Supervised Conditional Generative Adversarial Network [51.84251358009803]
  We present a novel self-supervised learning approach for conditional generative adversarial networks (GANs) under a semi-supervised setting. We perform augmentation by randomly sampling sensible labels from the label space of the few labelled examples available. Our method surpasses the baseline with only 20% of the labelled examples used to train the baseline.
  arXiv  Detail & Related papers  (2020-06-11T17:14:55Z)
• Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation [49.295165476818866]
  This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation. Existing approaches usually regard the pseudo label as the ground truth to fully exploit the unlabeled target-domain data. This paper proposes to explicitly estimate the prediction uncertainty during training to rectify the pseudo label learning.
  arXiv  Detail & Related papers  (2020-03-08T12:37:19Z)

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.