Title: Dual Alignment Framework for Few-shot Learning with Inter-Set and Intra-Set Shifts
Venue: NeurIPS 2025
Accepted: yes
Keywords: Few-shot learning

optimal transportation
Abstract: Few-shot learning (FSL) aims to classify unseen examples (query set) into labeled data (support set) through low-dimensional embeddings. However, the diversity and unpredictability of environments and capture devices make FSL more challenging in real-world applications. In this paper, we propose Dual Support Query Shift (DSQS), a novel challenge in FSL that integrates two key issues: inter-set shifts (between support and query sets) and intra-set shifts (within each set), which significantly hinder model performance. To tackle these challenges, we introduce a Dual Alignment framework (DUAL), whose core insight is that clean features can improve optimal transportation (OT) alignment. Firstly, DUAL leverages a robust embedding function enhanced by a repairer network trained with perturbed and adversarially generated “hard” examples to obtain clean features. Additionally, it incorporates a two-stage OT approach with a negative entropy regularizer, which aligns support set instances, minimizes intra-class distances, and uses query data as anchor nodes to achieve effective distribution alignment. We provide a theoretical bound of DUAL and experimental results on three image datasets, compared against 10 state-of-the-art baselines, showing that DUAL achieves a remarkable average performance improvement of 25.66%. Our code is available at https://github.com/siyang-jiang/DUAL.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel problem setting called Dual Support Query Shift (DSQS), which considers both inter-set and intra-set shifts, whereas the existing Support Query Shift (SQS) only addresses inter-set shift. Motivated by the idea that clean features can improve Optimal Transport (OT) alignment, the authors introduce a framework named Dual Alignment (DUAL) to tackle the DSQS problem.\n\nDUAL consists of several components. To extract clean features, it first trains a repairer module \n𝑅 to restore shifted images by learning the specific shift patterns. Then, to obtain a robust embedding function 𝜙, a hard example generator 𝐺 is trained while keeping \n𝜙 and the classifier 𝜃 frozen. Finally, 𝜙 and 𝜃 are jointly trained.\n\nDuring the inference phase, dual-regularized OT is applied to align dual shifts. Both theoretical analysis and empirical results demonstrate the superiority of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\n1. Strengths:\n(1) The paper introduces a novel problem setting called Dual Support-Query Shift (DSQS).\n\n(2) It proposes a dedicated framework to address the DSQS challenge.\n\n2. Weaknesses:\n(1) The tutorial material on Optimal Transport (OT) is insufficient and problematic. Although OT is not a new technique, many readers may not be familiar with it. The authors use notations such as \n𝜔 and 𝜋 without clear definitions, and the mixing of inconsistent symbols further hinders understanding.\n\n(2) Although the authors present their method in a step-by-step manner, the underlying intuitions and motivations behind each component are not sufficiently explained, which increases the difficulty of understanding the overall design.\n\n(3) Regarding writing quality, there are multiple grammatical errors and inconsistencies in notation. For example, the sentence “In metric-based FSL, a support set S consists of C different classes within each class c and |Sc| labeled instances.” (lines 115–116, page 3) is grammatically incorrect and needs to be revised. Additionally, inconsistent spelling such as “Interset” (line 185, page 5) versus “inter-set” elsewhere should be standardized.\n\n(4) Several key references cited in the paper are not published in prestigious conferences or journals, which reduces the overall credibility and persuasiveness of the work.\n\nQUESTIONS:\nTo improve the overall quality and readability of the paper, I recommend the following. First, the authors should briefly but clearly explain the fundamental elements of Optimal Transport (OT), including the meaning of key notations, to ensure accessibility for readers unfamiliar with the topic. Second, the intuitions and motivations behind each component of the proposed framework should be articulated more clearly and coherently, rather than merely describing the implementation steps. Finally, the writing quality should be significantly improved in terms of grammar, clarity, and consistency in notation and terminology.\n\nLIMITATIONS:\nYes, but they put  the discussion on limitations in Appendix §D.1 instead of the main text.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims at solving a more realistic few-shot learning challenge called Dual Support-Query Shift (DSQS), which accounts for both inter-set and intra-set distribution shifts. To address this, the authors propose DUal ALignment framework (DUAL) that combines a pixel-level repairer and an adversarial generator to obtain clean, robust features, followed by a two-stage optimal transport alignment to reduce distribution mismatches. The method is theoretically justified and empirically validated, achieving strong performance gains over baselines across multiple benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1.The paper provides solid theoretical analysis to support its design choices, including how the repairer and adversarial generator reduce intra-set noise and expand class margins, and how the dual-stage optimal transport improves distribution alignment and lowers classification risk. This adds depth and clarity to the proposed framework.\n\n2.The method demonstrates significant and consistent improvements over various few-shot learning approaches across multiple benchmarks, including CIFAR-100, mini-ImageNet, and Tiered-ImageNet. The gains are especially pronounced under challenging distribution shift settings, validating the practical effectiveness of the proposed solution.\n\nWeaknesses:\n\n1.In line 131, the variables $w$ and $\\pi$ used in the optimal transport formulation are not clearly defined in the main text. While readers familiar with OT may infer their meaning, it would improve clarity and self-containment if the paper explicitly defined these terms when first introduced.\n\n2.The paper relies heavily on optimal transport (OT) but gives only a minimal explanation. A brief background on OT and clarification of the solution process would greatly improve clarity for readers unfamiliar with this concept.\n\nQUESTIONS:\n1, Can the authors clarify whether the shifts used to train the repairer R are the same as those applied during DSQS testing, or whether there is any empirical analysis on how the type of training shifts affects performance?\n\n2, Although Table 5 reports the inference cost of the method, could you provide a comparison with baseline approaches to clarify how the added modules affect efficiency relative to existing methods?\n\n3, Could you provide a comparison of the parameter number or computational cost of your method (including repairer, generator, and dual OT) against baseline approaches to clarify whether the added components significantly increase model complexity?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to solve the problem of few-shot learning (FSL), where the goal is to achieve good generalization on query set, given a small support set. The authors claim to solve the issue of two shifts in support and query datasets, inter-set shifts and intra-set shifts. They propose dual alignment framework for feature embeddings to alleviate these issues, and they implement (1) a repairer module for inter-set and intra-set distortion rectification, and (2) a generator module for simulating hard examples which encourages purturbation. They validate their method on well-known few-shot classification benchmarks, comparing their method to other algorithms.\n\nSTRENGTHS AND WEAKNESSES:\n- Strengths\n  - Motivation behind few-shot learning and its issues are plausible, and the questions raised by the authors are appropriate. The authors' proposed approach is well-aligned to tackle these issues.\n  - Authors show the performance of their approach on well-known few-shot learning benchmarks, comparing their method to relatively recent few-shot learning baselines with similar (prototypical, exemplar) structure.\n  \n- Weaknesses\n  - Since there is a previous publication (PGADA [19]) with highly similar motivation and contributions, authors should clearly point out the differences and make their novelty more prominent.\n    - Additional missing reference with similar approach: \"Dual Adaptive Representation Alignment for Cross-Domain Few-Shot Learning\", Zhao et al., TPAMI 2023.\n\t- \"Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation\", Tseng et al., ICLR 2020.\n  - Since the main contribution of the paper is to deal with intra- and inter-set shifts for better generalization across few-shot tasks, experimental ablation for the cross-domain FSL scenario should be included. Although cross-domain FSL assumes domain shift between meta-training and meta-test tasks, its results should roughly quantify the generalizability of the proposed method.\n  - The generator G samples a hard augmented image $x_p$ from $P$ which is Gaussian, and the Gaussian perturbation seems to be too simple for real-world visual domains. Since there are many augmentation methods (MixUp, CutMix, AugMix, etc.) available, why are these methods not used?\n\nQUESTIONS:\nPlease refer to the weaknesses section for questions. The authors need further validation for the novelty behind their contributions.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript introduces DUAL, which tackles the challenging Dual Support-Query Shift (covering both inter-set and intra-set shifts) in few-shot learning. It extracts clean features via a robust embedding function combined with a pixel-level repairer, and then employs a dual-regularized optimal-transport approach to align the distributions of instances in the support and query sets. Extensive experiments on multiple datasets verify the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Extensive experiments validate the method on multiple datasets, with comprehensive ablation studies.\n\n2. Introducing the dual support-query shift scenario offers a certain degree of novelty.\n\n3. Clear writing flow. The overall narrative of the paper is smooth and well-structured.\n\nWeaknesses：\n1. Some essential experiments have not yet been reported. For instance, the paper targets both inter-set and intra-set shifts, certain experiments and visualizations do not convincingly isolate which operations address inter-set shifts and which tackle intra-set shifts.\n\n2. Methodological details remain under-specified. Prior work largely focuses on the support-query shift (inter-set shift); because this paper additionally introduces intra-set shifts, more fine-grained methodological details are needed to clarify exactly how intra-set shifts are mitigated.\n\n3. Presentation quality still needs improvement. The manuscript contains some minor errors throughout.\n\nQUESTIONS:\n1. Because the paper’s main contribution is to mitigate inter-set and intra-set shifts, please provide visualizations that clearly show which step alleviates inter-set shifts and which operation resolves intra-set shifts; this would better substantiate the central claim.\n\n2. The section on the two-stage OT approach is under-described: many implementation details are missing, and the experiments do not show that the two-stage method offers any advantage over a single-stage alternative.\n\n3. Methodological details remain under-specified. Prior work largely focuses on the support-query shift (inter-set shift); because this paper additionally introduces intra-set shifts, more fine-grained methodological details are needed to clarify exactly how intra-set shifts are mitigated.\n\n4. Some experimental aspects remain unclear: 1) How stable and robust are the hyper-parameters λ and β?.  2) In Appendix B (Details of Experiments), why were those particular network structures (e.g., REDNET) selected?\n\n5. Most core experiments use CIFAR-100, yet the “Computation Overhead” paragraph states, “For training, CIFAR-10 is the least computationally demanding…”. Is this a typo? The t-SNE visualization is also labeled as CIFAR-10.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}