Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement

• URL: http://arxiv.org/abs/2607.26607v1
• Date: Wed, 29 Jul 2026 08:29:47 GMT
• Title: Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement
• Abstract summary: We propose a two-phase transductive method operating over a frozen audio encoder.<n>First, each query sample is assigned a latent inlierness score that down-weights likely unknown-class samples.<n>The refined prototypes are then directly optimized on a transductive loss combining support cross-entropy, inlierness-weighted conditional entropy, and inlierness-weighted marginal entropy.
• Score: 11.626271838991883
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Few-shot Open-set audio classification requires classifying query samples from known classes with a few labeled support samples while rejecting query samples from unknown classes. Transductive inference jointly observes the full unlabeled query set to improve prototype estimation, yet standard transductive updates do not distinguish known from unknown query samples, leaving prototypes vulnerable to open-set contamination. Drawing on latent-inlierness weighting and decoupled scoring for unknown-class samples, we propose a two-phase transductive method operating over a frozen audio encoder. First, each query sample is assigned a latent inlierness score that down-weights likely unknown-class samples, so that prototype refinement is driven primarily by known-class evidence. The refined prototypes are then directly optimized on a transductive loss combining support cross-entropy, inlierness-weighted conditional entropy minimization, and inlierness-weighted marginal entropy maximization, while open-set rejection uses a prior-adaptive free-energy score that adjusts its threshold with the prior proportion of unknown-class samples, decoupling detection from classification. Experiments on three audio datasets show our method achieves state-of-the-art results for few-shot open-set audio classification under multiple experimental conditions.

Related papers

• Pre-Generation Hallucination Detection in Large Language Models via Soft-Target Attention Probing [0.9870126088784975]
  We introduce soft-target supervision based on the empirical answer error rate overally sampled outputs.<n>We adapt attention probing to the pre-generation setting, enabling the detector to selectively aggregate hallucination-relevant prompt representations.
  arXiv  Detail & Related papers  (2026-06-20T07:31:41Z)
• Combating Noisy Labels through Fostering Self- and Neighbor-Consistency [120.4394402099635]
  Label noise is pervasive in various real-world scenarios, posing challenges in supervised deep learning.<n>We propose a noise-robust method named Jo-SNC (textbfJoint sample selection and model regularization based on textbfSelf- and textbfNeighbor-textbfConsistency)<n>We design a self-adaptive, data-driven thresholding scheme to adjust per-class selection thresholds.
  arXiv  Detail & Related papers  (2026-01-19T07:55:29Z)
• PASS: Peer-Agreement based Sample Selection for training with Noisy Labels [16.283722126438125]
  The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. Current methodologies often rely on the small-loss hypothesis or feature-based selection to separate noisy- and clean-label samples. We propose a new noisy-label detection method, termed Peer-Agreement based Sample Selection (PASS), to address this problem.
  arXiv  Detail & Related papers  (2023-03-20T00:35:33Z)
• Neighborhood Collective Estimation for Noisy Label Identification and Correction [92.20697827784426]
  Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. Recent advances employ the predicted label distributions of individual samples to perform noise verification and noisy label correction, easily giving rise to confirmation bias. We propose Neighborhood Collective Estimation, in which the predictive reliability of a candidate sample is re-estimated by contrasting it against its feature-space nearest neighbors.
  arXiv  Detail & Related papers  (2022-08-05T14:47:22Z)
• Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels [87.48541631675889]
  We propose a two-stage clean samples identification method. First, we employ a class-level feature clustering procedure for the early identification of clean samples. Second, for the remaining clean samples that are close to the ground truth class boundary, we propose a novel consistency-based classification method.
  arXiv  Detail & Related papers  (2022-07-29T04:54:57Z)
• Imbalanced Classification via a Tabular Translation GAN [4.864819846886142]
  We present a model based on Generative Adversarial Networks which uses additional regularization losses to map majority samples to corresponding synthetic minority samples. We show that the proposed method improves average precision when compared to alternative re-weighting and oversampling techniques.
  arXiv  Detail & Related papers  (2022-04-19T06:02:53Z)
• S3: Supervised Self-supervised Learning under Label Noise [53.02249460567745]
  In this paper we address the problem of classification in the presence of label noise. In the heart of our method is a sample selection mechanism that relies on the consistency between the annotated label of a sample and the distribution of the labels in its neighborhood in the feature space. Our method significantly surpasses previous methods on both CIFARCIFAR100 with artificial noise and real-world noisy datasets such as WebVision and ANIMAL-10N.
  arXiv  Detail & Related papers  (2021-11-22T15:49:20Z)
• Reconstruction guided Meta-learning for Few Shot Open Set Recognition [31.49168444631114]
  We propose Reconstructing Exemplar-based Few-shot Open-set ClaSsifier (ReFOCS) By using a novel exemplar reconstruction-based meta-learning strategy ReFOCS streamlines FSOSR. We show ReFOCS to outperform multiple state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-07-31T23:23:35Z)
• Jo-SRC: A Contrastive Approach for Combating Noisy Labels [58.867237220886885]
  We propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution.
  arXiv  Detail & Related papers  (2021-03-24T07:26:07Z)
• A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detection [0.0]
  A novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. A preprocessing technique based on a new metric is devised in order to measure and mitigate the conflict between the evidences.
  arXiv  Detail & Related papers  (2020-12-04T09:16:35Z)
• Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching [69.24460241328521]
  Machine learning algorithms can be negatively affected by mismatches between training (source) and test (target) data distributions. We propose an unsupervised domain adaptation method that consists of aligning the first- and second-order sample statistics of each frequency band of target-domain acoustic scenes to the ones of the source-domain training dataset. We show that the proposed method outperforms the state-of-the-art unsupervised methods found in the literature in terms of both source- and target-domain classification accuracy.
  arXiv  Detail & Related papers  (2020-04-30T23:56:05Z)

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.