IPEC: Test-Time Incremental Prototype Enhancement Classifier for Few-Shot Learning

• URL: http://arxiv.org/abs/2601.11669v1
• Date: Fri, 16 Jan 2026 02:10:47 GMT
• Title: IPEC: Test-Time Incremental Prototype Enhancement Classifier for Few-Shot Learning
• Authors: Wenwen Liao, Hang Ruan, Jianbo Yu, Xiaofeng Yang, Qingchao Jiang, Xuefeng Yan,
• Abstract summary: Metric-based few-shot approaches have gained significant popularity due to their relatively straightforward implementation, high interpret ability, and computational efficiency.<n>We propose a novel test-time method called Incremental Prototype Enhancement (IPEC), a test-time method that optimize prototype estimation by leveraging information from previous query samples.<n>We ground this approach in a Bayesian interpretation, conceptualizing the support set as a prior and an auxiliary set as a data-driven posterior, which in turn motivates the design of a practical "warm-up and test" two-stage inference protocol.
• Score: 17.144931110395273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metric-based few-shot approaches have gained significant popularity due to their relatively straightforward implementation, high interpret ability, and computational efficiency. However, stemming from the batch-independence assumption during testing, which prevents the model from leveraging valuable knowledge accumulated from previous batches. To address these challenges, we propose a novel test-time method called Incremental Prototype Enhancement Classifier (IPEC), a test-time method that optimizes prototype estimation by leveraging information from previous query samples. IPEC maintains a dynamic auxiliary set by selectively incorporating query samples that are classified with high confidence. To ensure sample quality, we design a robust dual-filtering mechanism that assesses each query sample based on both global prediction confidence and local discriminative ability. By aggregating this auxiliary set with the support set in subsequent tasks, IPEC builds progressively more stable and representative prototypes, effectively reducing its reliance on the initial support set. We ground this approach in a Bayesian interpretation, conceptualizing the support set as a prior and the auxiliary set as a data-driven posterior, which in turn motivates the design of a practical "warm-up and test" two-stage inference protocol. Extensive empirical results validate the superior performance of our proposed method across multiple few-shot classification tasks.

Related papers

• Symmetric Aggregation of Conformity Scores for Efficient Uncertainty Sets [6.673032375204486]
  We propose SACP (Symmetric Aggregated Conformal Prediction), a novel method that aggregates nonconformity scores from multiple predictors.<n>SACP transforms these scores into e-values and combines them using any symmetric aggregation function.<n>We show that SACP consistently improves efficiency and often outperforms state-of-the-art model aggregation baselines.
  arXiv  Detail & Related papers  (2025-12-07T17:54:07Z)
• MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Paths [9.542597285477683]
  We propose a Model-Agnostic Test-Time paradigm (MATT) to unlock the predictive potential of trained CTR models.<n>To quantify the confidence of feature combinations, we introduce a hierarchical probabilistic hashing method.<n>We generate instance-specific inference paths through iterative sampling and aggregate the prediction scores from multiple paths to conduct robust predictions.
  arXiv  Detail & Related papers  (2025-10-10T02:22:55Z)
• Self-Improving LLM Agents at Test-Time [49.9396634315896]
  One paradigm of language model (LM) fine-tuning relies on creating large training datasets.<n>In practice, gathering large sets of data is inefficient, and training on them is prohibitively expensive.<n>We study two variants of this approach: Test-Time Self-Improvement (TT-SI) and Test-Time Distillation (TT-D)
  arXiv  Detail & Related papers  (2025-10-09T06:37:35Z)
• Reward-Shifted Speculative Sampling Is An Efficient Test-Time Weak-to-Strong Aligner [24.152878302325508]
  We introduce the reward-shifted speculative sampling (SSS) algorithm, in which the draft model is aligned with human preferences, while the target model remains unchanged.<n>Our algorithm achieves superior gold reward scores at a significantly reduced inference cost in test-time weak-to-strong alignment experiments.
  arXiv  Detail & Related papers  (2025-08-20T20:10:56Z)
• Enhancing Test Time Adaptation with Few-shot Guidance [62.49199492255226]
  Deep neural networks often encounter significant performance drops while facing with domain shifts between training (source) and test (target) data.<n>Test Time Adaptation (TTA) methods have been proposed to adapt pre-trained source model to handle out-of-distribution streaming target data.<n>We develop Few-Shot Test Time Adaptation (FS-TTA), a novel and practical setting that utilizes a few-shot support set on top of TTA.
  arXiv  Detail & Related papers  (2024-09-02T15:50:48Z)
• Adapting Prediction Sets to Distribution Shifts Without Labels [16.478151550456804]
  We focus on a standard set-valued prediction framework called conformal prediction (CP)<n>This paper studies how to improve its practical performance using only unlabeled data from the shifted test domain.<n>We show that our methods provide consistent improvement over existing baselines and nearly match the performance of fully supervised methods.
  arXiv  Detail & Related papers  (2024-06-03T15:16:02Z)
• Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting [65.21599711087538]
  Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample.<n>Prior methods perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications.<n>We propose an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples.
  arXiv  Detail & Related papers  (2024-03-18T05:49:45Z)
• Refine, Discriminate and Align: Stealing Encoders via Sample-Wise Prototypes and Multi-Relational Extraction [57.16121098944589]
  RDA is a pioneering approach designed to address two primary deficiencies prevalent in previous endeavors aiming at stealing pre-trained encoders. It is accomplished via a sample-wise prototype, which consolidates the target encoder's representations for a given sample's various perspectives. For more potent efficacy, we develop a multi-relational extraction loss that trains the surrogate encoder to Discriminate mismatched embedding-prototype pairs.
  arXiv  Detail & Related papers  (2023-12-01T15:03:29Z)
• Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning [54.61762276179205]
  We propose a novel contrastive learning approach, MMBS, for building robust VQA models by Making the Most of Biased Samples. Specifically, we construct positive samples for contrastive learning by eliminating the information related to spurious correlation from the original training samples. We validate our contributions by achieving competitive performance on the OOD dataset VQA-CP v2 while preserving robust performance on the ID dataset VQA v2.
  arXiv  Detail & Related papers  (2022-10-10T11:05:21Z)
• Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning [58.2091760793799]
  We propose a novel contrastive prototype learning with augmented embeddings (CPLAE) model. With a class prototype as an anchor, CPL aims to pull the query samples of the same class closer and those of different classes further away. Extensive experiments on several benchmarks demonstrate that our proposed CPLAE achieves new state-of-the-art.
  arXiv  Detail & Related papers  (2021-01-23T13:22:44Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)

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.