Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training

• URL: http://arxiv.org/abs/2506.17499v1
• Date: Fri, 20 Jun 2025 22:24:38 GMT
• Title: Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training
• Abstract summary: Metric-based models operate by computing similarities between query and support embeddings within a learned metric space.<n>A small set of labeled support samples is provided during inference to aid the classification of unlabeled query samples.<n>We propose a series of simple yet effective episode-specific, during-inference fine-tuning methods for metric-based models.
• Score: 13.5196633635749
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In few-shot classification tasks (so-called episodes), a small set of labeled support samples is provided during inference to aid the classification of unlabeled query samples. Metric-based models typically operate by computing similarities between query and support embeddings within a learned metric space, followed by nearest-neighbor classification. However, these labeled support samples are often underutilized--they are only used for similarity comparison, despite their potential to fine-tune and adapt the metric space itself to the classes in the current episode. To address this, we propose a series of simple yet effective episode-specific, during-inference fine-tuning methods for metric-based models, including Rotational Division Fine-Tuning (RDFT) and its two variants, Iterative Division Fine-Tuning (IDFT) and Augmented Division Fine-Tuning (ADFT). These methods construct pseudo support-query pairs from the given support set to enable fine-tuning even for non-parametric models. Nevertheless, the severely limited amount of data in each task poses a substantial risk of overfitting when applying such fine-tuning strategies. To mitigate this, we further propose to train the metric-based model within an optimization-based meta-learning framework. With the combined efforts of episode-specific fine-tuning and optimization-based meta-training, metric-based models are equipped with the ability to rapidly adapt to the limited support samples during inference while avoiding overfitting. We validate our approach on three audio datasets from diverse domains, namely ESC-50 (environmental sounds), Speech Commands V2 (spoken keywords), and Medley-solos-DB (musical instrument). Experimental results demonstrate that our approach consistently improves performance for all evaluated metric-based models (especially for attention-based models) and generalizes well across different audio domains.

Related papers

• PRiSM: Prototype Regularization for Few-Shot VLMs [32.74737063872657]
  Training-free few-shot adaptation methods have gained significant attention recently in the context of Vision-language Models.<n>We introduce PRiSM, a class-prototype regularization that can be deployed as a plug and play module on top of any existing baseline method.<n>Extensive experiments show that PRiSM improves several training-free baselines, with large gains when dealing with severe class imbalance and high numbers of classes.
  arXiv  Detail & Related papers  (2026-07-20T11:08:10Z)
• Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies [50.39041754816285]
  We propose a framework for learning multi-validation weights that jointly adapts data selection to both the downstream task and the specific model.<n>Our approach achieves performance comparable to or exceeding full-dataset tuning while using only 30% of the training samples on GSM8K.
  arXiv  Detail & Related papers  (2026-05-10T17:30:16Z)
• FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning [2.750124853532831]
  We propose FB-NLL, a feature-centric framework that decouples user clustering from iterative training dynamics.<n>Our framework consistently outperforms state-of-the-art baselines in terms of average accuracy and performance stability.
  arXiv  Detail & Related papers  (2026-04-21T17:51:58Z)
• Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning [71.30276778807068]
  We propose a unified framework that strategically coordinates sample pruning and token pruning.<n>Q-Tuning achieves a +38% average improvement over the full-data SFT baseline using only 12.5% of the original training data.
  arXiv  Detail & Related papers  (2025-09-28T13:27:38Z)
• ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts [71.91042186338163]
  ALoRE is a novel PETL method that reuses the hypercomplex parameterized space constructed by Kronecker product to Aggregate Low Rank Experts.<n>Thanks to the artful design, ALoRE maintains negligible extra parameters and can be effortlessly merged into the frozen backbone.
  arXiv  Detail & Related papers  (2024-12-11T12:31:30Z)
• Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting [0.0]
  We try to push the understanding of different fine-tuning strategies for large language models (LLMs) We compare state-of-the-art methods like vanilla fine-tuning and Pattern-Based Fine-Tuning (PBFT) on pre-trained models across two datasets, COLA and MNLI. Our findings suggest that these alternative strategies can exhibit out-of-domain generalization comparable to that of vanilla FT and PBFT.
  arXiv  Detail & Related papers  (2024-05-21T20:08:52Z)
• Rethinking Few-shot 3D Point Cloud Semantic Segmentation [62.80639841429669]
  This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS) We focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. To address these issues, we introduce a standardized FS-PCS setting, upon which a new benchmark is built.
  arXiv  Detail & Related papers  (2024-03-01T15:14:47Z)
• Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning [58.837146720228226]
  Few-shot learning aims to recognize novel queries with limited support samples by learning from base knowledge. Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains. We propose to address the cross-domain few-shot learning problem where only extremely few samples are available in target domains.
  arXiv  Detail & Related papers  (2023-06-18T09:52:16Z)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)
• Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification [11.035878821365149]
  We propose a novel Adaptive Meta-learner via Gradient Similarity (AMGS) to improve the model generalization ability to a new task. Experimental results on several benchmarks demonstrate that the proposed AMGS consistently improves few-shot text classification performance.
  arXiv  Detail & Related papers  (2022-09-10T16:14:53Z)
• Classification Performance Metric Elicitation and its Applications [5.5637552942511155]
  Despite its practical interest, there is limited formal guidance on how to select metrics for machine learning applications. This thesis outlines metric elicitation as a principled framework for selecting the performance metric that best reflects implicit user preferences.
  arXiv  Detail & Related papers  (2022-08-19T03:57:17Z)
• MetaAudio: A Few-Shot Audio Classification Benchmark [2.294014185517203]
  This work aims to alleviate this reliance on image-based benchmarks by offering the first comprehensive, public and fully reproducible audio based alternative. We compare the few-shot classification performance of a variety of techniques on seven audio datasets. Our experimentation shows gradient-based meta-learning methods such as MAML and Meta-Curvature consistently outperform both metric and baseline methods.
  arXiv  Detail & Related papers  (2022-04-05T11:33:44Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

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.