Dual Alignment Framework for Few-shot Learning with Inter-Set and Intra-Set Shifts
Authors: Siyang Jiang
Rui Fang
Hsi-Wen Chen
Wei Ding
Guoliang Xing
Ming-syan Chen
Venue: NeurIPS 2025 poster ()
Keywords: Few-shot learning
optimal transportation
Primary area: applications

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.

Dual Alignment Framework for Few-shot Learning with Inter-Set and Intra-Set Shifts
Siyang Jiang', Rui Fang", Hsi- Chen', Wei Ding"
Guolaing Xing Ming-Syan Chen
'he Chinese University of Hong Kong
National Taiwan University
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 chal- lenging in real-world applications. In this paper, we propose Dual Support Query Shift (DSQS), 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 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. Addition- ally, incorporates 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 theoretical bound of DUAL and experimental results on three image datasets, compared against 10 state-of-the-art baselines, showing that DUAL achieves a re- markable average performance improvement of 25.66%. Our code is available at https://gi thub. com/ haam//iyang-aaa jaalyynnggggggnDiiii iDng/
Introduction
Few-shot learning (FSL) addresses the challenge of limited labeled data by extracting features and leveraging the similarity between support and query sets, rather than training separate classifier for each class. This characteristic makes FSL suitable for tasks with scarce data and unseen scenarios, exemplified by methods such MatchingNet [45], which assigns query example the label of its most similar counterpart in the support set. Conventional studies on FSL often focus on cross- domain settings [37, 52], where distribution shifts occur between training and testing data [41, 26). To mitigate this issue, previous approaches have enhanced robustness through data augmentation [49, 50] or adversarial training [18, 53].
However, these methods assume that each support or query set is internally consistent, i.c., they share the same domain during testing. In practice, Support-Query Shift (SQS) [3] frequently arises due to differences in environments (e.g.. foggy vs. high-luminance scenes) or capture devices (e.g., mobile phones vs. SLR cameras), leading to misclassification. To address SQS, Bennequin et al. [3] first employed optimal transportation (OT) [7] to align embeddings into a shared latent space. More recently, Jian al. [21] introduced noise-aware data augmentation scheme to alleviate distribution misalignment, while Aimen et al. [1] highlighted the growing distribution mismatch between support and query sets during testing.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Yet prior studies on SQS have primarily addressed inter-set shifts, i.e., differences between support and query sets, while often overlooking intra-set shifts, where instances within the same set experi- ence distinct disturbances. These intra-set shifts further complicate the problem by blurring decision boundaries. We term this overlooked issue Dual Support-Quee both inter-set and intra-set shifts during meta-testing. Intra-set variations can be as significant as inter-set shifts, posing substantial challenges for existing SQS mitigation methods.
Shift (DSQS), which encompasses
As shown in Fig. 1, panel (a) illustrates that conven- tional FSL, with no shift between support and query sets, exhibits clear decision boundaries. These bound- aries, however, become blurred under either inter-set shifts (b) or intra-set shifts (c). In the inter-set case, samples of the same class in the support and query sets may still cluster, yet the comparison module fails to classify query instances correctly because the two sets lie in different domains. In the intra-set case, instances of the same class within a set are scattered across do- mains, preventing clustering. Consequently, even if query is classified to a nearby support instance, it may not belong the same class. When both shifts occur simultaneously, as shown in (d), the boundaries blur further, severely reducing generalization and lead- ing to poor inference performance under DSQS.
oDo O
PPV ooo
PPP
PDP
(1)
(b)
(c)
(4)
An illustrative example of Support-Query Shifts (SQS), where col- indicate classes, circles denote support samples, and triangles denote query sam- ples: (a) no shift, (b) inter-set shift, (c) intra-set shift, and (d) dual shifts (DSQS).
Figure
To address the DSQS problem, we propose the DUal ALignment Framework (DUAL), designed to mitigate the adverse effects of two types of distribution shifts: inter-set shifts (between support and query sets) and intra-set shifts (within each set). Based on our theoretical analysis, DUAL alleviates the challenges optimal transportation by combining a robust embedding function with pixel- level repairer to obtain clean features. The repairer, trained on predefined shifts that simulate query distortions, rectifies them by minimizing the feature-space distance between original and repaired data, thereby counteracting both inter-set and intra-set shifts.
In addition, the robust embedding function is trained using a generator that adversarially produces perturbed "hard" samples that are less similar in the embedding space yet still correctly classified. DUAL then employs a dual-regularized optimal transport approach, which identifies class-oriented anchors within the support set by minimizing intra-class distances and aligns the distribution of other instances to these anchors using optimal transport with a negative entropy regularizer. Additional query samples are incorporated as anchors to enhance the robustness of the transportation plan.
The main contributions of this work are summarized as follows.
We propose the Dual Support-Query Shift (DSQS) challenge, which investigates the inter-set and intra-set shift problems in FSL. We theoretically show that both types of shifts can misguide the domain alignment process under optimal transportation.
To address DSQS, we introduce the DUal ALigmment Framework (DUAL), which leverages repairer together with a robust embedding function adversarially trained by a generator to obtain clean features. These features are then used align support and query distributions through dual- regularized optimal transportation.
We provide both theoretical and empirical analyses of DUAL. In particular, we theoretically char- acterize its behavior, and extensive experiments demonstrate that DUAL consistently outperforms 10 state-of of of-te--------r methods, achieving an average improvement of 25.66% across three benchmark datasets.
2 Related Work
Support- Querry Shift in Few-shot Learning Conventional few-shot learning (FSL) methods can be broadly categorized into three groups: dallucinationn-assed optimization-base and metric-based approaches [36]. Hallucination- and optimization-based methods typically aim to obtain strong initial model that can quickly adapt to new tasks with minimal updates [24, 33, 47, 48). Our work is more closely related to metric-based FSL, which focuses on learning a similarity-based classi-

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fier [45, 42, 17]. Representative methods include MatchingNet [45], which uses pairwise metrics, and ProtoNet [42], which relies on class-wise metrics assign labels query samples based on their proximity to support set representations. More recently, the support-query shift (SQS) set- ting has emerged, where the support and query sets are drawn from different domains. To address this challenge, TP [3] leverages optimal transport (OT) to align the support and query distributions. However, image perturbations can distort the transport plan and lead to suboptimal alignment. To mitigate this issue, PGADA [21] integrates a regularized OT framework with an adversarial gener- ator to produce challenging examples for self-supervised adaptation. Similarly, AQP [I] enhances model robustness by generating challenging instances but relies projection method rather than adversarial generation.
Robust Few-shot Learning Robust few-shot learning aims defend against adversarial samples by developing robust embedding functions [12, 35, 28]. One research direction focuses on the use of adversarial queries. AQ [12] employs adversarial queries to enhance model robustness, while
LCAT [29], a meta-learning based method, achieves comparable performance to AQ with reduced training time. In addition, Dong et al. [10] propose non non-meta-learing method that learns robust embedding function and applies a post prot-procsssing feature purifier to reduce computational overhead further. SimpleFS [43] trains robust network on base samples and classifies new samples by as- signing them to the nearest base-category centroids in the feature space. Another line of research leverages high-frequency spectrum information or self-distillation, both of which
sensitive to adversarial perturbations in those regions [46, 27, 35]. For instance, LFI [27] shows that publicly available robust models prefer the low-frequency spectrum, thereby avoiding other adversarial per- turbations. SSL-ProtoNet [28] employs self-distillation to build robust classifiers, while RAS [35] uses adversarial self-distillation to achieve robustness without explicitly using adversarial samples.
3 Preliminary
Few-shot Learning. Conventional FSL methods can be broadly categorized into three groups: hallucination-baased optimization-basee port set S = Usec S consists of C classes, where each class contains |S labeled instances. The objective of FSL is to correctly assign each element of the query set Q = Uoee Q to one of these C classes.
and metric-based approaches. In metric-based FSL, a sup-
Let Φ denote the embedding model, where o(x) E IRd maps data point x into a d-dimensional feature space. The model 6 is trained on labeled dataset D {xx,, y4) y,)) where 2, denotes data point and yi its associated label. The learning of & follows empirical risk minimization (ERM):
min (( (T(((()))) y)),
6.0
where 0 is trainable classifier mapping the embedding d(x) to label y, and L is the loss function. Using the learned embedding model o, data points in the support set (Ir,, E S) and query set (Ig.j Q) are transformed into their feature representations ((x,,) and Φ(Fg.))-
Optimal Transportation. Optimal transportation (OT) aims to realign distributions by minimiz- ing the cost of transporting one distribution to another. This technique addresses discrepancies between datasets, enhances model generalization from training to testing, and yields more robust feature representations [7, 21]. A key concept in OT is the transport cost, which quantifies the effort required to move probability mass between distributions, often measured using metrics such as the Wasserstein distance.
Suppose there are finite samples in both the support set E and the query set E Q. Discrete OT employs empirical distributions to approximate the probability measures,
Σ
80,55
Big =
Σ 8955
where 52,; and 64.5 denote Dirac distributions. The discrete OT problem can then be formulated as
arg min
Σ
(( T T( Ins),
(2)
Sa Boi Ma
3

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Training Phase
Inference Phase
Shift
(((+))
Support Sat
Query Set
By
Close ((x)
L
Training Pixel-level Repair
(())
1g
Distant
(Zzg)
#
Lidee
Noise
Align Intra-set shift
Lite
Φ
2
Luwi
2p
(#,) y
Training Robust Embedding Function
Align Inter-set shift.
Overview of DUAL. In the training phase, train pixel-level repairer and robust embedding function, which are then utilized during inference. In the inference phase, the objective is to align both intra-set and inter-set shifts using n' and x².
Figure 2:
where w( ( is the ground cost between samples.
To compute the transport plan π², Sinkhorn's 's algorithm [8] is often applied. With the optimal plan, the embeddings of the query set Φ(xg.)) are transported to ((يg))) via barycentric mapping [7]. adapting the query set to the support set:
)(Xg)) = Ex T' (" ((Eai))
(3)
Ex,,es T (Ioistag))
where (( denotes the transported embedding of T.J. This allows the distance metric
M M(o(z,,), (( to be accurately computed within a shared embedding space.
Dual Support-Query Shift in FSL. Conventional FSL typically assumes domain shift between the training and testing phases, i.e., Drsin n Dees = Ø. However, in the meta-testing phase, an additional challenge arises: the support set and the query set Q within each task may themselves be drawn from different distributions, denoted as Dss and DQ. As result, the embeddings of support samples (xx,) and query samples ((z,)) may lie in different spaces, leading an inter-set shift that causes misclassification [3, 21]. This phenomenon, known as the Support-Query Shift (SQS) problem [21, 3, 1]. has been widely studied. Yet, prior work primarily focuses on inter- set differences while overlooking variations within each set. We refer to this more general and challenging scenario as the Dual Support--uuryy Shift (DSQS) problem. addition to inter-set shifts, DSQS accounts for intra-set shifts, where different instances within the same set (e.g., 4i, qj Q) may originate from distinct distributions. similar issue arises in the support set. Moreover, under DSQS, the distributions of instances across support and query sets can also be mutually disjoint due to multiple unknown shifts, making alignment particularly difficult.
4i
4 DUal ALignment Framework (DUAL)
To address DSQS, we present the DUal ALigmment Framework (DUAL) for both training and inference. The key idea of DUAL is that clean features facilitate more reliable OT alignment. We first motivate DUAL by showing that both inter-set and intra-set shifts may mislead the OT plan (64.1). To mitigate this issue, DUAL first obtains clean features through pixel-level repairer and an adversarially trained embedding function ($4.2). It then reduces shifts using dual-regularized OT framework, aligning instances to handle both inter-set and intra-sct variations ($4.3).

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In the training phase (Left Part of Fig. 2), we develop pixel-level repairer R and a robust embedding function & for inference. Trainable network components are highlighted in pink. The yellow block illustrates corrupting the original data I with shifts, which are then repaired by R. The repaired data 2y compared with using cosine similarity I, to optimize R. In the orange block, l.g denotes negative cosine similarity, while Lude and Lori (Eq. (11)) represent KI. divergence losses. generator perturbs a to produce a hard example zp, which is less similar the embedding space but remains in the same class. These hard examples are generated via Ludv and Lg while Lady and Lori jointly train the embedding function
In the inference phase (Right Part of Fig. 2), features of support and query samples are extracted by R and Φ. As in Fig. colors denote classes, while circles and triangles represent support and query features. Transported support features (dashed circles) are aligned with dlass-wise centroids (gray dashed circles with anchor symbols) according the OT plan x'. Subsequently, transported query features (green dashed circles) are obtained through the OT plan n².
4.1 Motivation
While earlier OT-based FSL methods [21, 3] assume a single domain on each side (one-to-one alignment), the DSQS setting involves multiple domains in both the support and query sets. Follow- Gaussian mixture, with the overall distribution of Gaussians.
ing [30], we model each domain as a component
represented class- class-weighted
Assumption 1 (DSQS Gaussian- follows ((x) ~ NNe,, )c where {s, タf denotes the support or query domain. The class prior P(c) is shared across domains, but the component means Pe,oo and covariances Le,o may differ.
For every class c E C, the latent feature 6(x) E R
Aggregating over classes yields the global moments
Ho = Σ P()) E. = P(c(EEns + (Mo,o - 1o) Mo) - 1o)
(4)
Let W2(S, Q) denote the 2-Wasserstein distance between the empirical feature distributions of the support set S and the query set Ω.
Proposition 1 (OT cost under first-order Gaussian approximation). Approximating each domain by its first-order moments gives
W2( = |
1all2
+ u(E
2 11/2)/11))))
(5)
inter-set mevan gap
inter-set covariance gap
Thus, the transport cost grows monotonically with (i) the inter-set mean gap 1//4 Mqll2, and (ii) the intra-set spreads tr( tr(E,) and tr tr(Eg).'
Proposition 2 (Error of transported embeddings). Let Ö(Iq)) be the transported query embedding obtained from the clean OT plan in Eq. (3), and &o (Ig.)) its noisy counterpart. Assume additive Gaussian noise 17 N0,,0311 is independently injected in both support and query domains {s, q). Then,
B[(ld(1..)) do (5. = d(o + +
Higher noise levels og. og therefore increase the risk of mismatched OT plans, ultimately degrading classification accuracy.
Summing up. Propositions and 2 highlight two key sources of error under DSQS: (i) domain mis- alignment (mean/covariance gaps), and (ii) feature noise. Our DUAL framework addresses both: (i) it contracts domain gaps via dual-regularized OT, and (ii) it learns a noise-tolerant embedding, thereby reducing (S,S, Q) and stabilizing transported features.
If n and Mg commute, common high digenninnnii approximation [6], the cross term vanishes, reducing the trace expression to tr tr. F.)).
5

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4.2 Dual Adversarial Training
During training, we adopt a two-level adversarial strategy to obtain clean features for subsequent dual alignment, thereby enhancing model robustness by suppressing input noise and improving em- bedding quality. Specifically, we first train a repairer R to remove noise from input images. Then, we adversarially train embedding function ø using generator G, which produces "hard" examples to strengthen its robustness.
Training a Pixel-level Repairer. To mitigate pixel-level noise, we first train a repairer R to restore shifted images, thereby helping extract cleaner features. The key idea of training R is minimize the embedding-space distance of Φ between features before and after repair. In this way. R preserves the original semantic structure while reducing noise. Notably, R provides & with low-noise inputs, which theoretically tightens the feature-noise upper bound o' < Lép (detailed in Lemma 1).
As illustrated in the yellow block of Fig. 2, we add predefined shift to the original data and the repairer network R generates the restored data 2y. The training objective is:
min E min M((xx,),
(6)
where M denotes the comparison metric in FSL, such as Euclidean distance or cosine similarity. We encourage (xx,) to closer to o(x) so that It learns to correct the imposed shift, producing cleaner representations that reduce semantic distortion during inference.
To train R, we minimize the embedding space distance between the repaired data Ty- and the original data z, formulated as
min M(((2z))
In this way, the repairer R learns to correct diverse shifts with single model, showing that our framework is a bias-agnostic solution applicable real-world scenarios.
Training Robust Embedding Function. After corrupted data are restored by the repairer R. we further enhance the robustness of the embedding function é through adversarial training with hard examples generated by network G. As shown the orange block (upper part) of Fig. 2, G is trained to produce perturbed samples Tp that are less similar to the original data point x in the embedding space, by maximizing the comparison loss. To make e resilient to such perturbations, we adopt the following minmax objective:
min E. E max M(((E))
Zp
In practice, we sample a batch of augmented candidates {xp} and select the one that maximizes the loss L. so that is updated against the hardest instance. As illustrated in the orange block (bottom part) of Fig. 2, we realize G as a semantic-aware generator:
Zp G(()) s.t. [10(0(=))) - (((())) <,
(9)
where G perturbs into Ip while preserving its class semantics. We adopt dropout [14] for stochasticity. Unlike conventional adversarial training that perturbs inputs via i.d.. noise (c.g.. Tp ~ M(x, T)) [39, 47], our generator encodes semantic structure directly, requiring fewer sam- ples to achieve convergence [13].
We enlarge the embedding distance between and its perturbed counterpart Tp. To ensure that G retains sufficient class semantics, we enforce that the generated example Zp can still be classified as the same label y, using KL divergence [37] regularizer. In practice, we adopt the soft-label vector form for the KL term. The generator is therefore trained with the following objective:
max G
M ((((Gz))) 6(x) - (L((((((())))v)
(10)
Following [21], we optimize both and via stochastic gradient descent (SGD). During this stage, the parameters of the embedding functions ó and 0 are kept fixed [2].

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Once the hard examples are derived, we train the embedding functions to acquire more robust fea- tures, As shown the orange block (bottom part) Fig. 2. we jointly minimize the empirical risk of both the original data x and the perturbed example xp using KI. divergence:
min À AKT(0(((=))) 6.0
(1 X) K (T(00((z)))
(11)
Toors
Ladv
where À controls the trade-off. During inference, DUAL extracts robust features through the repairer R and the embedding function @. While their combination may appear straightforward, our design enforces a clear separation of roles: R acts as a pixel-level denoiser, whereas @ operates at the feature level. Since G challenges e by generating semantically perturbed samples, A must instead preserve proximity to the original semantic space. Jointly training R and & would thus lead to conflicting objectives and hinder convergence, making the decoupled design erucial for stability.
4.3 Dual Regularized Optimal Transportation
Previous OT-based methods for SQS assume a single-domain alignment, which becomes insufficient under the DSQS setting, highlighted in Proposition 1. To address this, propose dual regular- ized optimal transportation scheme for inference. After obtaining clean features from the repairer R and robust embedding @, extend classical OT with negative-entropy regularization stabilize the transport plan.
Intra-set Alignment via Regularized OT. As illustrated in the purple block of Fig. 2, first perform intra-set alignment by transporting support samples S to their class-wise centroids S (gray dashed circles with anchor symbols), yielding a plan n' and a transported support set S' {2 This step reduces intra-class variance and alleviates intra-set shifts. Formally,
x' arg min
B ( () F., + (1 3) ग (x..,, T.,kk log T.,k), (12)
ES
TA,ES
where S denotes class-wise centroids and B controls the smoothness of the transport plan.
Inter-set Alignment with Anchored OT. After obtaining n' and the transported support set S', we align the query set Q with S'. Specifically, we build the queryanchor cost matrix
CO. =woooas), o(x(,)))
(13)
where w(.::) is the ground cost used in Eq. (12). We then reuse the same regularized OT formulation, replacing (S,S) with (') and obtain ' via Sinkhorn scaling.
In summary, the dual OT scheme produces two transport plans, x' (supportcentroid) and x" (query- support), which jointly mitigate intra-set and inter-set shifts under DSQS. Notably, the first-stage alignment is only relevant for multi-shot cases; in the one-shot setting, intra-class centroid align- ment is required.
Theoretical Analysis
Here, we analyze four key quantities that characterize the behavior of the DUAL framework: (i) the post-repair noise variances o' and d';- (ii) the conss-ooniitioall covariances Ec,o for E {s,q); (iii) the 2- 2-Wasserstein distance W2(S, Q) between the aligned support and query distributions: and (iv) the classification risk Pr[h((r) # y] under a I-Lipschitz nearest- prroost-po classifier h. Detailed proofs can be found in Appendix A.
We first show that variance contraction is achieved by the repairer.
Lemma (Variance contraction). Assume that: 1) is i--iipscitz i.e., (olu)-((v)]la < T. o1l2: 2) The repair network R satisfies an expected pixel-space MSE of e² = E, [((r) - x x1.. Then the post-repair feature noise in each domain satisfies o'. < LEp where o E (s, q).
In the one-shot scenario, where only one sample exists in the support set, we apply regularized optimal transport to align the support set and query set.
7

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Lemma 1 shows that pixel-space denoising reduces feature-space noise linearly via the Lipschitz continuity of Φ, thereby controlling stochastic variation within domains. To prevent adversarial shifts from inflating class-conditional spreads, we next establish margin enlargement result.
Lemma 2 (Margin enlargement). Assume dual adversarial training is used with a target margin parameter 0, following marrgn--asse objective such as:
min max cos ((x), O(G())) + ALa((G())
(14)
where Lels (e.g., CE or KL divergence) penalizes label changes. Then for any ||ő(x) ((G()))l 2 and G(x) preserves the class label of x. Consequently, tr (T(E..o) - tr --rrr-aa (Eadd) Z for all c and {s.q)-
While Lemma controls random noise, Lemma 2 guarantees a deterministic margin between clean
and adversarial features of the same class, thereby contracting each coasiiooniii covariance ellipsoid. Together, the two lemmas imply that the combined effect of Repair R and Generator G tightens the geometry of both domains. We quantify how this geometric tightening reduces the Wasserstein distance between the support and query distributions.
Theorem (Contracted OT bound). Under the assumptions of Lemmas 1-2, define =T= Ki ST= Ey: PT= = ft for t Sinkhorn iterations with damping 3 > 0. Then,
E WW 2) I (1 - (1-PT) ||4 - Mall 2NT + tr((2, + =Zg - -2n2 +2LVdET.
(15)
shrunk mean gap
shrunk covariance
Eq. (15) reveals that the support-query transport cost contracts by (at least) 2NT in the means and 2x² in the covariances, up to a vanishing solver residual pr and the small repair term LEr. A reduced Wasserstein distance, in turn, strengthens the generalization guarantees of Lipschitz classi- fiers. The next corollary makes this connection explicit.
Corollary (Classification risk). Let h be a 1-Lipschitz nearest- nearest-prototye prototype classifier in the aligned space, and define A = mineze l|pe - Her 1/2- If A 2KT, then under the same assumptions Theorem 1, the classification risk satisfies
W Q) A
Pr(M(x)) + v)
exp "[LET)²
K
(16)
Eq. (16) decomposes the error into distribution mismatch term, W2 2)/ and robusiness term that decays exponentially with the squared margin n²z.. Hence, the alignment strategies simul- tancously minimise domain divergence and enlarge the safety margin around each class prototype. yielding provably lower risk.
establishes that pixel-level repair reduces feature noise via Lipschitz conti- nuity, while Lemma 2 shows that dual adversarial training enforces feature separation and contracts class-conditional covariances. Building on these, Theorem proves that DUAL reduces the Wasser- stein distance between support and query distributions after repair and alignment, up a solver residual. Finally, Corollary bounds the classification error, revealing that generalization is jointly governed by inter-class separation and the robustness margin. Together, these results provide rigorous foundation for how DUAL achieves robust alignment and lowers classification risk under domain shift.
Summing up. Lemma
Experiment
We evaluate DUAL against 10 state-of-the-art baselines on three public datasets. Due to space limitations, the pscudo-codes, dataset details, baseline descriptions, and implementation details are provided in Appendix B.
Setup. To validate our framework, we evaluate on three standard benchmark datasets: (1) CIFAR- 100 [23]. (2) mini-ImageNNt [44], and (3) Tiered- Ieage-- [38] for FSL. We compare against 10 state-of-the-art FSL methods, divided into three categories: (i) four conventional FSI. base- lines: MatchingNet [45], ProtoNet [42], TransPropNet [31], and FTNet [9]; (ii) three support-query

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Table 1: Quantitive results of DUAL.
CIFAR-100
ImageNet
Tiered ImageNet
CIFAR-100
mini mini-ImugeNNt
Fiered-Imagehh
Methods
5-shot
MatchingNet [45]
30.26 20.3s
43.62_o.47
30.01
40.35...33
56.24_1..7
35.05..
ProtoNet [42]
28. 20.30
43.8 43.84 20.44
30.15+u.41
41.59 11.59:0.41
59.8 83 59.83.xu.42
13.41 +0.43
PransPropNet [31]
1o.3t
24.22_o.22
24.18 10.32
37.061o..0
25.98 1o.2u
35.48 10.37
FTNet [9]
22.36=0.21
37. 97.04=0.44
22.01++00
26.19
49.14 +0.10
24.50+00.2
AQ [12]
35. 86 1o.54
31.59_o.44
30.2441..m
53.93 10.55
43.85 1,0.40
38.541...
TP [3]
30.89=0.42
45.66=0.65
29.34+0.48
45.50+0.s7
62.32
41.92++0.30
PGADA [21]
34.90=0.45
50.37
28.47
49.4 49.45
61.09
40.73
AQP [1]
31.68=0.99
60.59=0.43
30.40+n.40
45. 00.+0..
42. 65.0.67
15.34+0.00
RAS [35]
36, 06..9_...3
50.40.0.3a
31.052..00
50.02:0.a1
63.952..40
43.98
SSL-ProtoNet (28)
=0.00
28. =.0.09=o.s0
29.31+0.48
48. 18.7444..77
96.56+0.a2
35.65
DUAL-P
38. 20
53.00_..6o
34.29
54.47.20.c
67.83gn.40
47.81
DUAL-M
39.35
5-1. go.4
35.29 10.37
50.11
61.01....2
12.96 10.38
Table 2: Ablation studies and In-depth analysis of DUAL.
Techniques Variants
CIFAR-100
mini-ImageNet
Ticred-ImageNet
CIFAR-100
mini- Iani-ImageNet
TTered-Imagelle
T-shot
5-shat
dual AT OT
27.43
43.932u.a7
27.85
41.97 20.41
63.60go 45
40.48
w.o. dual AT w.o, dual OT
31.36:004
53. 0..59
30.76:0.4
42.00+0.64
66. 06.2220..
40.81:0.0
34.63 40
40.88
97.54
53.20g9.44
68.69
30.57 1.031
w/o,
25.98.2.26
13.7440.77
29.32
47.10
61.2220.7a
43.95
w.o. R
27.47
44.12.0.:5
26.73
35.05
62 12.333...0
37.92.0.32
Fixred
38. 38.48
55.35
31.122a..4
52.47 12.477 #0.47
66. 26.91=0.4r
5..44....0
Enc shift
34156::0.s
49.37:10.50
24.26
45.98
62.5520.00
29.11g0.29
TP DUAL-P
32.03
48. 5..5.
28.52 28.52
46. 13......0
64.255...4
41. 41.22::0.38
38.93 so
53.00g0.59
31.29
51.17 +0.50
67.83go.0 40
47.81g0.4
shift baselines: TP [3]. PGADA [21], and AQP [1]: (iii) three adversarially robust FSL baselines: RAS [35], AQ [12], and SSL-ProtoNet [28]. Since DUAL is model-agnosticc adversarial alignment framework, we implement it with different classifiers, e.g., ProtoNet (DUAL-P) and MatchingNet (DUAL-M).
Quantitative results. Table shows that DUAL consistently outperforms the four conventional baselines (MatchingNer, ProtoNet, TransPropNet, and FTNet), achieving an average improvement of 24. 24.16%. These methods fail to address the distribution shift between support and query sets, which DUAL effectively realigns using adversarial training. Compared to adversarially robust FSL methods, DUAL (including DUAL-P and DUAL-M) relatively surpasses SSL-ProtoNet by 35.65% on average by aligning distributions at both the task and instance levels. Although TP and AQP also leverage optimal transport, they remain sensitive to small perturbations and relatively suffer 12.93% and 21.86% accuracy loss, respectively. Overall, DUAL achieves up 25.66 25.66% higher accuracy than state-of thtte-of-the-att methods on average by reconstructing information lost due to instance-level shifts.
Ablation Studies. We conduct ablation studies on DUAL-P with ProtoNet (similar trends hold for MatchingNet). As shown in Table 2, removing both dual adversarial training (Dual AT) and dual regularized optimal transport (Dual OT) causes a substantial performance drop, confirming their necessity. Specifically, Dual AT improves accuracy by average of 11 11.59%, with the largest gain observed on Tiered-ImageNet in the 1-shot setting (from 30. 70% to 34.29%), where clean features crucial for reliable alignment (see $4.1). Adding Dual OT further enhances performance, with improvements of up to 17.24% in 5-shot accuracy on Tiered-ImageNet, as it explicitly mitigates distribution shifts in the feature space through optimal transport.
In-depth Analysis. We further analyze the roles of the generator (G) and repairer (R). Removing G relatively reduces accuracy by 11.80% showing its importance in generating adversarial pertur- bations for robustness. Excluding R causes 22.54% relative drop, highlighting its critical role in repairing features for alignment. Fixing G during training relatively lowers performance by 3. 3.20%, indicating that trainable G better captures iistance-speciff variations. Training only the encoder on perturbed images without R yields 7.06% gain, but still underperforms the full model. Adding R on TP [3] can boost performance by 5.24%, confirming its role mitigating shifts and improv- ing generalization. These results validate the complementary roles of G and / in robust few-shot learning under domain shifts. Due to space limitations, we provide additional visualization effects in the appendix, comparing cosine similarity to illustrate how features contribute to alignment. For

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Table 3: The results of adopting DSQS and CD-FSL.
In-Domain
Out-of-Domain
Method
ImageNet-IK IK
Aircraft
Deserihable Textures
Fungi
MSCOCO
ProtoNet [42]
20. 47.447
35.49
18.82
2
TransPropNet [31]
10.91
11.8620-29
19.85
10.13
10.134031
AQ [12]
45.94
19.53+0.40
30.11
18 +8.09
20.46+0.20
PGADA [21]
22.67
17.9110.48
36.04.0.47
22.7210.63
26.6911.64
DUAL.PP
25.66 25.66+0.54
21.83+..57
40.69
26.28+0.68
31.6640.71
Out-of-Domain
Method
Omniglot
Quick Draw
VGG Flower
CUB-200-2011
Traffic Signs
ProtoNet [42]
17.0910.50
28.431..73
47.9 47.9810.72
24.9410.63
29.32
TransPropNet [31]
10.15+0.29
10.6340.31
14.7040.42
11.85+0.35
10.90
AQ [12]
12.98
18.21
49.60
26.33
20.38
PGADA [21]
32.8110.2
40.97
49.93
24.71
31.6310.6s
DUAL-P
50.71
53.58+0.77
60.45+0.78
31.17+0.73
37.33+0.74
example, on mini- ImageNe,,, adopting intra-OT and inter-OT increases the alignment similarity by approximately 14.6% and 20.5%, respectively.
FSL. Cross-Domain (CD) FSL introduces domain gap be- tween the training and testing sets, whereas DSQS imposes dual shifts at meta-test time: inter-set shifts between support and query sets, and intra-set shifts within each set. To evaluate both settings in unified framework, we conduct experiments on four representative baselines using Meta-Datased which spans ten public image datasets across diverse domains. Following the protocol of [44], we meta-train on the ImageNet- IK training split and evaluate on ImageNet- 1K (in-domain) as well as Numbers of ways, shots, and query images are randomly sampled as in [16]. As shown in Table 3, DUAL-P achieves the best performance across all domains. These results indicate that DUAL generalizes effectively under both in-domain and out-of-domain conditions, and remains robust to the dual shifts characteristic of DSQS.
When DSQS Meets
the remaining datasets
7 Discussion and Conclusion
Real-world Complex Tasks. DUAL can be applied to complex real-world tasks such as quality monitoring and beverage deterioration monitoring [19, 20J. where distribution shifts are common. By aligning distributions in the embedding space, our framework is able to maintain robustness in these settings. For example, adapting DUAL to object detection or segmentation requires only modifications to the training objective, such as the choice of loss functions. The key concept of DUAL is first to extract clean features via Dual AT and then perform improved alignment through Dual OT, which together address both inter-set and intra-set shifts. Pre-trained models such as CLIP can also provide cleaner features due to their strong generalization capabilities [25], but they introduce higher computational costs and slower inference in real-world applications.
Limitations and Future Work. Overall, this work introduced DSQS a challenging FSL scenario characterized by both inter-set and intra-set distribution shifts, and proposed the DUal ALignment Framework (DUAL) to mitigate them. Theoretical and empirical results demonstrate that DUAL outperforms ten baselines across multiple datasets. Looking ahead, we envision ex- tending DUAL to broader vision tasks, exploring stronger embedding functions, and investigating additional techniques for addressing DSQS. Nevertheless, DUAL still leaves room for refinement and integration of more advanced techniques. Incorporating recent advances in robust embedding learning or distribution alignment could further improve its effectiveness. For example, more sophis- ticated adversarial training strategies [4] or advanced OT formulations [40, 32] may better capture the nuances of real-world data distributions.
Acknowledgment
This work was supported by the National Science and Technology Council (NSTC), Taiwan, the Ministry of Education (MOE). Taiwan, under Grants NSTC 1 114-223-EE-002-009 NSTC 114-2221- E-002-180-MYY3, MOE 1 1141895504, MOE 141.9009, Research Grants Council (RGC) of Hong Kong, China, under the General Research Fund (GRF) Grant No. 14203420 and Collaborative Research Fund (CRF) Grant No. C1045-23G.
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Justification: In this paper, we first defined DSQS, a novel challenge in FSL caused by inter- set and intra-set distribution shifts. To address this, we propose the, DUAL, which mitigates these shifts by generating clean features and optimizing the transportation plan. Theoretical and experimental results demonstrate that DUAL outperforms 10 baselines across multiple datasets. We believe that the abstract and introduction can reflect the paper's 's contributions and scope.
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A Proof
A.1 Preliminary: Optimal Transportation
Episode data and embeddings Let the support set be S = ( with labels (Us,i) and the query set be Q = { 1ー=1- The embedding model ø : X -R yields features 2 o(r,,)) and Fq.j ( ). Unless otherwise stated, we use uniform masses a, = -1 and by = 1 for support and query, collected as a E Am and E An.
Ground cost and cost matrix Let w(+;.) be ground cost between two features (e.g., squared Euclidean w(u, v) = ||u-v|l 2 or cosine distance w(u, v) = Tu Tu The episode cost matrix is C E R with entries
(u.e)
C₁, ud 25,0r z9.).
Discrete Kantorovich OT (primal form) A transport plan nonnegative matrix и RR that moves probability mass from S to Q while satisfying marginal constraints:
rIn = a, T mm b.
The (unregularized) OT problem minimizes the total cost
min (C, s.t. Tl, = a. π 1m b. #20
which upper-bounds the squared 2- Wassersteii distance between the empirical feature distributions of 5 and Q
Entropy-regularizer OT and Sinkhorn scaling For stability with few samples and noisy fea- tures, we adopt an entropy-regulanie objective consistent with our framework:
min 3 (C, F20
3)
Σ
Tsi log Tiy s.t. Iln a, 1m =b
(.j
where 3 E (0, 1] trades off fidelity to C (large 9) and smoothness of æ (small (). This is equivalent
to the common form (C, E Tigglog وijj - 1) with temperature - = - Gibbs kernel K exp(-C/r) elementwise, the optimizer is obtained by Sinkhorn iterations:
1-8 3. Defining the
et b 2 (K (Kuu), u), ut a (Kv).
diag(u) K diag(v),
where denotes elementwise division.
Barycentric projection (feature transport) Given an optimal plan n, we align query features to the support geometry via barycentric mapping:
Σm Tifj Rzgá
2
and compute the metric (...) (e.g., cosine or Euclidean) aligned space for classification.
In our two-stage alignment ($4.3), we instantiate this machinery twice: (i) an intra-set plan x' that transports support instances class-wise centroids, producing S', and (ii) an inter-set plan x² that aligns queries to S
A.2 Proof of Proposition 1
Proof. When both of the marginals Hss My Gaussian distributions, the problem can be greatly simplified. A closed-form solution exists. Denote the mean and covariance of f4, and Σ,, respec- tively. Let S, be two Gaussian random vectors associated with μas Mgg respectively. Then, the cost becomes
E[JiS Q13) = Q1
||m, m,,l?
(17)
where 5 5 m, and Q = Q - my are the zero-mean versions and Q. We minimize (17) over all possible Gaussian joint distributions between X and Y, resulting in
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min Im= mmml= + trace(E, + -2 - 2K)
Ea KT
K E
20
with = =K (SS"). }. The constraint is semidefinite, so the above problem is a semidefinite program ming (SDP). turns out that the unique minimizer in closed form achieves the minimum
= / -
1/2
with minimum value
(Hss: Mgy) 1| m1 112 + trace
(18)
+2in 2(2 "
(19)
The consequent displacement interpolation 14 is a Gaussian distribution with mean my = (1 t)m, + tmy and covariance
E = (1- "/ 1/1/2.
(20)
A.3 Proof of Proposition 2
We first provide Lemma to prove the Proposition Lemma 3. The error of the transportation cost
W Wa 1n) S W (Hss S Wolps: Mg) + v d(o? + oz),
denotes the original support and query set distribution Mo (No where Wo (pa, Pa) W (Ug No Hs and pi being perturbed with Gaussian noises o, and 0g.
Note that |-1 is the absolute value, and # is the convolution operator. Based on Lemma 3, we estimate the error of transported embedding ((x,,)) in Eq. (3).
Proof. The left-hand side inequality immediately follows because W is non-increasing under con- volutions, since N Voz+o?" =Na. * *Na,, where * is the convolution operator.
On the right side of the inequality, we adopt Saatorovich-Ruuinssee duality write the optimal transport as follows.
W(us, = sup [! $1
Epr. [w]] - Eyp, [w]
(21)
Wa (Ps sup [1
EpjaNo, [wo] - Engy [wo]
(22)
where 11-11zip is the Lipschitz norm. Letting w" be optimal for W WHuss Mg), we obtain, Wa (Ms,,gg) = Epr,Ns, [w=]] - ExpgeeN,, [w=].
(23)
Let Xs Mb Zo No, as independent random variables, we have, E,,, [w"] Ep,,NN, [w=]] =E(w" (X.)] Elw" (X + Z.)l
(24)
<1 |1 113] Vda..
where the last inequality uses ||w* 1lzap 1. d is the dimension of the embedding vector. Similarly, Xg ~ Mg. Zg ~ No, as independent random variables, we have,
|E,,[1w'] E, E,,,Noo [w=
(25)
= =Elw" (X,)) Elwx (X + Zg)l
< 2, 11:] = v dd
By inserting Eq. (24) Eq. (25) to Eq. (23), and Cauchy-Sehwaad inequality, we concludes the proof.
D
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In the following, we prove the proposition.
Proof. Base on Lemma 3, barycentric coordinate is defined as follows,
"T" (Rt,,) (qus)) Ey x" (Ke,i, Tas)
No.
(26)
Let Xg ~ Myr X2 My No, as independent random variables,
B|x, xX = Oy.
(27)
where Xq (2(() and x denotes the t-th dimension of random variable X² and Xg. respectively.
Combining Eq. (26) and Eq. (27). the perturbed distribution X, ~ Ms No, * No, Iss N oooo
B|X, x,)= V + oz))
(28)
As the noise level, i.e., da and og increases, it is more likely to mislead the transportation plan and alleviate the model's performance.
A.4 Proof of Lemma
Proof. Fix o = s (the query case is identical). The noisy feature of a sample is 2 ((R(x)) and the noisefree feature is = (x)) By Lipschitz continuity, ||ž 2||2 L [((xx) 1/12. Squaring and taking the expectation over x ~ D. yields:
E 2 21: < L B|(((r) - x112 = I =
The left-hand side is o,2, so of S LEp.
A.5
Proof of Lemma
Proof. The optimality of the generator follows from the fact that, for a fixed encoder Φ, the cosine loss remains strictly larger than its minimum when []((x) ( margin constraint must be met to avoid increasing the classification loss Lels- Conversely, given an optimal generator, the encoder seeks to maximize the cosine loss by pushing ((G(z)) away from d(x), thereby enforcing the margin k. This adversarial separation has the effect of tightening the classconditional covariance: letting Me,o B[ø(z) | y c| denote the class center, adding adversarial examples ((G(()) at distance x decreases the empirical second moment around Pe,o by at least 2
k; thus, the
A.6
Proof of Theorem
Proof. We derive the bound by considering the combined effects of mean alignment, covariance reduction, feature noise, and Sinkhorn solver inaccuracy. First, dual-adversarial training enforces minimum margin Kr between each sample and its adversarial counterpart in feature space. As result, each centroid (i.e., domain mean) moves toward the other by up to Kr, leading to at least a 2KT reduction in the squared Euclidean distance between the support and query means due to the reverse triangle inequality. Similarly, since adversarial samples are at least Kr away from their respective class centers, the empirical second moment around each class center is reduced by at least n27, and across both domains, the total covariance trace is decreased by least 2n².. Next, the feature noise introduced by the pixel-level repair network is bounded in expectation by LeT per dimension, and across d-dimensional embedding, this contributes additional distortion bounded by 2LVd V when considering used to approximate the optimal transport cost yields a (1 - Pr conotraccedd estimate of the true cost, where pr = ett depends on the number of iterations & and regularization strength | [11]. Combining these effects yields the desired bound in Eq. (15).
sample from each domain. Finally, the entropic Sinkhom solver
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A.7 Proof of Corollary
Proof. We derive the classification risk bound by considering two failure modes: transport emror and repair noise. Let be query sample and x' its aligned prototype. Since h is Lipschitz, the classification decision satisfies
[(xx) h(2')l S 11((x) ((x))ll2.
(29)
and by Markovs inequality, the probability that h(z) * h(z²) is bounded by [[llo()) (0000)0(()))))))) ((/))
where A is the minimum inter-class prototype separation.
This yields the first term. For the second term, Lemma implies that the repaired feature di (2))
deviates from the true feature o(x) by sub-Gaussian variable with parameter LET, so the proba- bility that this deviation exceeds the margin KrT bounded by exp(- -k2 / via Hoeffdings
inequality. A misclassification occurs if either the transport error moves the sample outside its class region or the repair noise shifts the feature beyond the margin; by the union bound, this yields the combined upper bound in Eq. (16).
B
Implementation Details
B.1 Pseudo Code of DUAL.
Algorithm DUal Alignment Framework (DUAL)
Require:
Training dataset D. comparison module M, learning rate trade-off parameters A1 and A2, an arbitrary shift S, support set S, query set Q.
Ensure: Embedding model Φ, repairer R. Optimal Plan n²,
I: Initialize generator G, repairer R,
2: Initialize embedding model Φ classifier 0.
3: # Training Stage
4: for (a, in D do fixed ④, 8, update G,
6: Ip = G(x), Ie S Ir = H(xx)
7:
Lg - ( 6(z)), Lr M ((((x)),
8:
Trddu = K (0((((()))))))
# Generated lesy similar data points.
10:
G + G - V + Ladu)), t - R
11:
# fixed G. R, update 6,
12:
Ip G(x).
13:
Lors KK((0(((=)) u)), Lade (L(O(o(z,)).
14:
classifyynng the generated samples correctly.
15:
à o -- nV (ALori + (1 Laduv
16:
8 - v (ALors + A)dndo)
17: # Inference Stage
18: Solve the Eq. 12 obtain x² and m²
19: S, = o (R(S)) , 2 = (R))
20: S₁= [s Sy. S S1) 2 = 0,) 27)
21: (f., f. y-} in S/ L
22: Hy = M M
B.2 Details of Datasets
CIFAR-100 100 consists of 60, 000 three-channel square images of size 32 x 32, evenly distributed in 100 classes. Classes evenly distributed in 20 superclasses. We employ 19 image transfor- mations [51], each being applied with 5 different intensity levels, to evaluate the robustness of model.
mini-ImageNNet contains 60, 000 square images with three channels size 224 224 from the ImageNet dataset with 64-classes training set, a 16-classes validation set, and a 20-classes test set [45]. Similar to 100, Imageeet also has the same transformations proposed by [15] to simulate different domains [15].
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Tiered-Imagelln [38] contains 779,165 three- channechannel 84 x 84 images, grouped into 34 higher- level nodes 608 classes. The nodes are partitioned into 20. 6, and 8 disjoint sets of training. validation, and testing nodes, and the corresponding classes form the respective meta-sets.
Meta-dataset [44] contains public image datasets of diverse range of domains: ImageNet- Ik, Omniglot, FGVCAircraft, CUB-200-2011 Describable Textures, QuickDraw, FGVCx Fungi, VGG Flower, Traffic Signs, and MSCOCO. Each dataset has train/val/test splits.
B.3 Details of Baselines
MatchingNet [45] measures the pairwise cosine similarity between the support set and the query set and assigns the same class of the support example to the query example.
ProtoNet [42] uses Euclidean distance classify queries to the prototype embeddings, i.e., averaging the embeddings of all support examples in the same class.
TransPropNeet [31] is extension of ProtoNet, which utilizes graph neural network of labels, leveraging information about local neighborhoods.
FTNET [9] is meta-learning framework that estimates the distribution between the train- ing and testing sets transductively.
AQ [12] is a robust FSL baseline designed produce adversarially robust meta-learners and investigate the causes of adversarial vulnerability.
TP [3] combines the ProtoNet, optimal transport, and transductive batch normalization solve the support-query shift in few-shot learning.
PGADA [21] reduces optimal transportation errors by learning from self-supervised hard examples and using negative entropy regularization.
AQP [1] aims to create more challenging virtual query sets by adversarially perturbing the query sets, inducing a distribution shift between support and query sets. AQP can regarded as the SOTA method in support-query shift few-shot learning using episodic training.
SSL-ProtoNet [28] is a metric-based few-shot learning approach that combines self- supervised learning, Prototypical Networks, and knowledge distillation to leverage sample discrimination effectively.
RAS [35]. is robust FSL baseline that employs high-level feature matching between base class data without the need for adversarial samples.
B.4 Details of Implementation
Following [3], report the average top-1 accuracy score with 95% confidence interval from 2000 runs. In addition, we conduct the tasks of 1-shot and 5-shot with 16-target, i.e., 1 or 5 instances per class in the support set and 16 instances in the query set, in CIFAR-100, Imng-Ima,eNNe, and Tiered- ImageNet. Same with [21], we use a 4-layer convolutional network as an embedding function 6 on As general adversarial training
CIFAR-100, ResNet18 for mini-ImageeNtt, and Tiered-ImageNie framework for few-shot learning, we combine DUAL-P with two classifiers, i.e., ProtoNet [42] and MatchingNet [45], in the testing phase. As for our repairer Rt adopted adjusted REDNET- like[34] structure, composed of a 4-layer encoder-decoder structure and 2 convolutional layers. In practice, we also adopt optimal transport [21] and self-supervised learning by deploying the NT-Xent Loss [5] on unlabeled data from the testing set. The learning rate 17, batch size 6, and embedding dimention d are set to le
128, 128, respectively. Besides, SGD with Adam optimizer [22] adopted to train the model in 200 epochs with early stopping. Grid search is adopted to select the trade-off parameter in the objective function, i.e., 0.5, 0.5 for best performance. Note that, for simplicity, most AQP baselines are evaluated under the SQS setting for comparison since DSQS a harder setting for AQP. addition, we re-implemented RAS for evaluation. Also, we adopt the transductive batch normalization [31] on TransPropNet, FTNET, TP, PGADA, and our framework. Most of experiments are conducted on workstation equipped with a NVIDIA GeForce RTX 4090 GPU (24 GB), Intel Core 1990KK CPU, and 128 GB of memory.
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Table 4: Cosine similarity of visualizations in different datasets. Higher is better.
Dataset
Original
Add Shifts
Adopting intra-OT
Adopting inter-OT
CIFAR-100
0.6954
0.5076
0.6338
0.7224
Imageet 0.6519
0,6083
0.6971
0.7332
Tiered-ImageNee
0.6758
0.6117
0.6515
0.7038
Discussions
C.1 Visualization Impact of Multiple Shifts
We provide a visualization of the impact on multiple shifts. As shown in Fig. 3, we can see that real-world images frequently dis- play multiple shifts within the same set. In particular, the multiple shifts, such as blur, noise, weather, and digital distortions, im- pact image quality and classification perfor- mance. Individually, each shift degrades the image, but combined shifts (e.g.. Blur + Noise or All) significantly distort the vi- sual features, making the image more chal- lenging to interpret. These compounded shifts enlarge the data distribution and pose greater challenges for models, especially when the shifts are unpredictable or un- known, leading reduced robustness and accuracy. This highlights the importance of addressing multiple shifts improve model performance.
Original
Blur
Noise
Weather
Digital
Weather
Digital
Blur Noise
All
Visualization of the impact multiple shifts. Multiple shifts make the image harder to clas- sify and enlarge the distribution compared to the orig- inal image, especially when such shifts are unknown.
Figure 3:
C.2 Visualization Analysis in Embedding Space
To quantify how method mitigates inter- and intra-distribution shifts, report cosine distance, ie., cosine similarity as a alternative visualization between support and query embeddings before and after applying the method, rather than relying on large-scale t-SNE plots, which are unstable and sensitive to hyperparameters. Specifically, we select large-scale samples from the CIFAR-100, mini- datasets construct support and query sets from different classes
ImageNet, and Viered-Imagelt and extract their embeddings to compare the cosine similarity of these samples. The key difference in this comparison is whether or not dual optimal transportation (intra-OT and inter-OT) applied. As shown in Table 4. we observe that the distances are closer when dual OT is used, demonstrating that our approach effectively reduces the distribution shift. We believe these experiments provide comprehensive evidence supporting our conclusions. In particular, applying intra-OT recovers meaningful portion of the lost consistency but does not fully return the original level. Inter-OT provides the most robust recovery, outperforming intra-OT on every dataset and narrowing the gap to the original the most. The effect is especially pronounced on the more heterogeneous dataset, suggesting that aligning relationships across samples is particularly effective when variability is higher.
C.3 Computation Overhead of DUAL
We conduct experiments on the average time of computational overhead for each component in one epoch of 5-way 1-shot. As shown in Table the computational overhead analysis highlights the significant variation in training and testing time across datasets and methods. For training, CIFAR- 10 the least computationally demanding, requiring only 0.52 hours for 100 epochs, compared to 18.50 and 14.72 hours for mini-ImageNet and Tiered-ImageNet, respectively, indicating the higher complexity of the latter datasets. During testing, the per-epoch time remains minimal for all datasets, with CIFAR-I 10 being the fastest at 0.003 hours, followed by 0.009 hours at Tiered- Tmered-l and
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0.045 hours at mini- mini-ImageNet.. Notably, the computational cost is justified by the accuracy improve- ments observed the corresponding methods, suggesting that the overhead remains acceptable for practical applications. Future work should focus optimizing these methods to further reduce the time complexity without compromising performance.
Table 5: Computation Cost of DUAL in Training and Inference Phase Time in the training phase denotes the wall-clock time of 100 epochs (Hours). Time in the inference phase denotes the infer- ence time in each epoch (Hours).
Methods
CIFAR-100
Maii--mageNee Training Cost
Hiered-ImageNee
I
23%
29%
27%
G
15%
33%
32%
Ø
62%
38%
41 %
Time
0.52
18.50
14.72
Inference Cost
Dual OT
14%
3%
11%
R
28%
8%
11%
Time
0.003
0.045
0.009
C.4 Broader Impact
DUAL tackles the critical challenge of Dual Support Query Shift (DSQS) in few-shot learning (FSL), significantly enhancing the alignment of distributions under both inter-set and intra-set shifts. By delivering robust performance in highly dynamic and unpredictable environments, DUAL has the potential to make machine learning systems more adaptable, resource-efficient, and accessible. These advancements hold promising applications in fields such as healthcare and autonomous driv- ing. However, the adversarial training employed in DUAL, while designed for robustness, could potentially inspire misuse in crafting adversarial attacks on other machine learning models. Re- searchers and practitioners should remain vigilant about ensuring ethical use of such techniques. Overall, DUAL represents a step forward making few-shot learning more robust and capable of handling real-world challenges. Its broader impact lies in improving the reliability, adaptability, and accessibility AI systems across diverse domains. However, it essential to remain mindful the ethical and environmental considerations associated with the framework, encouraging responsible research and deployment practices.
27


Reviews:
[{"review_id": "MitBZhPWlT", "reviewer": "Reviewer_H8EC", "summary": "The 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.", "questions": "1. 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.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Most of my concerns have been resolved, and the authors have promised to address the remaining issues in the revised manuscript. As a result, I have raised my score to 4.", "rebuttal": "Thank you for the reviewer’s comments. We have noticed that many of the concerns raised stem from misunderstandings of our work. In the following rebuttal, we have addressed them point by point.\n\n\n**Q1 & Q2 & Q3:** *Thank you for your comment. We summary the comment and listed as the following 2 question. (1) Missing Methodological and implementation details and (2)  Effects and Visualizations of detailed relieving inter- and intra distribution shift.*\n\n**A1 & A2 & A3:** \n\n* (1) Missing Methodological and implementation details.\n    * (1-1 Methodology Details ) We acknowledge that our main paper provided a less detailed description of the introduction to dual optimal transportation. While this concept may be familiar to readers with prior knowledge (as noted by Reviewer J495 and Reviewer q9yF), we plan to include a more comprehensive explanation in the revised version to improve readability and understanding for a broader audience.\n    *  (1-2 Implementation Details) In the implementation details, we will provide additional illustrations of optimal transportation and have also provided our reproducible code to further clarify our paper.\n\n* (2) Effects and Visualizations of detailed relieving inter- and intra distribution shift. \n   * (2-1 Detailed Effect) To validate the effectiveness of our framework, we conducted 8 experiments, including 3 ablation studies and 5 in-depth analyses, as shown in Table 2. Additionally, to further evaluate the impact of alleviating inter- and intra-distribution shifts in detail, we provide an experiment under the 5-way 5-shot setting, specifically focusing on their removal, **as shown in Table R1**.\n   * (2-2 Visualizations) We have provided t-SNE visualizations after adopting our framework in Fig. 4. To further illustrate the effect of alleviating inter- and intra-distribution shifts, we use cosine similarity to measure the distance between embeddings after applying our method. Specifically, we select several samples from the CIFAR-100 dataset to construct support and query sets from different classes and extract their embeddings to compare the cosine similarity of these samples. The key difference in this comparison is whether or not dual optimal transportation (dual OT) is applied.  **As shown in Table R2**, we observe that the distances are closer when dual OT is used, demonstrating that our approach effectively reduces the distribution shift. We believe these experiments provide comprehensive evidence supporting our conclusions.\n\n> Table R1 Impact of Removing Single-Stage OT\n|          Method          |  CIFAR-100  | mini-ImageNet | Tiered-ImageNet |\n|:------------------------:|:-----------:|:-------------:|:---------------:|\n|      w./o. dual OT       | 50.76+-0.38 |  65.61+-0.39  |   44.46+-0.38   |\n| w./o. intra-OT ($\\pi'$)  | 52.12+-0.37 |  66.10+-0.38  |   45.82+-0.39   |\n| w./o. inter-OT ($\\pi^*$) | 52.21+-0.31 |  66.75+-0.40  |   46.20+-0.40   |\n|           DUAL           | 54.47+-0.40 |  67.83+-0.40  |   47.81+-0.41   |\n\n> Table R2 Cosine Similarity of Visualizations\n|  Method   | Orginal | Add Shifts | Adopting intra-OT | Adopting inter-OT |\n|:---------:|:-------:|:----------:|:-----------------:|:-----------------:|\n| CIFAR-100 | 0.9424  |   0.7289   |      0.8549       |      0.8728       |\n\n\n---\n**Q4:** *Unclear experimental aspects, e.g., hyper-parameters and REDNET structures.*\n\n**A4:** We employed a grid search approach to explore the stability and robustness of our hyperparameters, as detailed in **Table 7, Appendix C.6**. We have tested several combinations of $\\lambda \\in (0.1, 0.5, 0.9)$ and $\\beta  \\in (0.1, 0.5, 0.9)$. Our findings indicate that the best performance in our setting is achieved when $\\lambda=0.5$ and $\\beta=0.5$. Regarding the REDNET structure, its use is primarily due to its widespread adoption as a robust image restoration  architecture [1-3]. Additionally, we want to emphasize the flexibility of our DUAL framework. Notably, other approaches, such as GANs or vision transformers, can also be substituted for REDNET while maintaining the compatibility and effectiveness of our framework.\n\n[1] SAVSR: Arbitrary-Scale Video Super-Resolution via a Learned Scale-Adaptive Network. (AAAI 2024).\n\n[2] FADE: A Task-Agnostic Upsampling Operator for Encoder–Decoder Architectures.(IJCV 2025).\n\n[3] Fast and accurate single image super-resolution via information distillation network. (CVPR 2018)\n\n---\n**Q5:** *Typos in the Appendix.*\n\n**A5:** Thank you for pointing this out. All experiments are conducted on CIFAR-100. We will thoroughly review the entire paper, including the Appendix, and make the necessary revisions."}, {"review_id": "nACqa3Zeh6", "reviewer": "Reviewer_VWnr", "summary": "This 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.", "questions": "Please refer to the weaknesses section for questions. The authors need further validation for the novelty behind their contributions.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "- 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?", "was_revised": true, "final_justification": "Most of the issues raised by the reviewers were addressed by the authors to some degree, and I am inclined to raise my rating of the paper.", "rebuttal": "Thank you for the reviewer’s comments.  In the following rebuttal, we aim to clarify the questions and have provided detailed responses to address each concern comprehensively.\n\n**Q1:** *Clarify the difference between DUAL and PGADA [19]*.\n\n**A1:** Thank you for your comments. While our motivation is similar to PGADA, our work makes three significant contributions that distinguish it from PGADA:\n- Dual Support-Query Shift (DSQS) Challenge: In our framework, we are the first to identify and propose the Dual Support-Query Shift (DSQS) challenge.\n- Theoretical Support: We provide theoretical evidence demonstrating that both inter- and intra-distribution shifts can misguide the domain alignment process, which has not been addressed in PGADA.\n- Novel Framework: We introduce a new framework, DUAL, which incorporates a dual adversarial training scheme and a dual optimal transportation scheme to effectively address the DSQS challenge.\nThese contributions highlight the originality and practical value of our work compared to PGADA. **For more details, please refer to our Introduction, Page 2, Line 73-83.**\n\n--- \n**Q2:** *Clarify the difference and experimental ablation for the cross-domain FSL (CD-FSL).*\n\n**A2:** Thank you for your comments. We have clarified the differences between DSQS and CD-FSL in **footnote 2 on page 2**. While CD-FSL assumes a domain shift between meta-training and meta-testing tasks, a key distinction is that it typically considers the shift to originate from a single domain [1-3]. In contrast, DSQS addresses a more complex and realistic scenario where shifts can arise from multiple domains. This means that instances within either the support set or the query set may belong to different domains, introducing a higher level of diversity and complexity. As a result, DSQS represents a fundamentally different and more challenging problem compared to CD-FSL.  To further strengthen our discussion, we will update the related work section to include discussions on [TPAMI 23] and [ICLR 20] in the context of CD-FSL.\n\n[1] A Closer Look at the CLS Token for Cross-Domain Few-Shot Learning. NeurIPS 2024.\n\n[2] Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations ECCV 2022.\n\n[3] Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty NeurIPS 2022.\n\n---\n\n\n**Q3:** *Why not use more complex augmentation methods such as MixUp, CutMix, AugMix.*\n\n**A3:** Thank you for your comments. We agree that more complex augmentation methods, such as MixUp, CutMix, or AugMix, could be incorporated into our framework, as they can serve as plug-and-play components to replace the generator $G$. However, it is important to emphasize that the primary contribution of DUAL is the introduction of a novel framework specifically designed to address the DSQS challenge, rather than the development of advanced, high-performance data augmentation techniques. Our focus is on providing a flexible and effective solution for mitigating DSQS, which could be further enhanced by integrating such augmentation methods if desired."}, {"review_id": "vQztm7GXge", "reviewer": "Reviewer_J495", "summary": "This 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.", "questions": "1, 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?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The authors have addressed my main concerns in the rebuttal. I am satisfied with their responses and will keep my original score.", "rebuttal": "Thank you for your thoughtful feedback. We appreciate your suggestions and will address them to improve the clarity and accessibility of our work.\n\n**Q1:** *More descriptions of Optimal transportation.*\n\n**A1:**  Regarding the variables $w$ and $\\pi$ in line 131, we agree that explicitly defining these terms in the main text would enhance clarity and ensure the paper is more self-contained. While readers familiar with optimal transport (OT) may infer their meanings, we will revise the manuscript to provide clear definitions and explanations when introducing these terms. In addition, we also acknowledge that the paper relies heavily on optimal transport (OT), and a more detailed explanation of its background and the solution process would aid readers unfamiliar with the concept. To address this, we will expand the related sections to include a concise yet comprehensive overview of OT, along with a step-by-step clarification of the solution process. This will ensure that the paper is more accessible to a broader audience.\n\n---\n**Q2:** *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\n**A2:**  Thank you for your insightful question. To clarify, we strictly separate the shift types used during training and testing to avoid any potential data leakage, adhering to the SQS protocols [1]. Specifically, all shifts used to meta-train the repairer R are entirely disjoint from those used in DSQS meta-testing. This ensures a fair and rigorous evaluation of our framework. \n\n---\n\n**Q3:** *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\n**A3:** Thank you for raising this important point. To address your concern, we have conducted a comparison of inference costs between DUAL and baseline methods on Tiered-ImageNet, using the 8-target 1-Shot inference setup.\n\nAs shown in Table R1-J495, it reveals that DUAL (ProtoNet) increases inference time by only **6%** compared to the vanilla ProtoNet. This level of overhead is comparable to MatchingNet (**7%**) and on par with other SQS baselines, such as TP (**6%**) and TransPropNet (**3%**). These results demonstrate that our method introduces only a minimal additional inference cost while achieving significant improvements in addressing the DSQS challenge.\n\n>Table R1-J495: Inference cost of DUAL.\n|    Method    | Average Latency (ms) | Relative Cost (%) |\n|:------------:|:--------------------:|:-----------------:|\n|   ProtoNet   |         5.80         |       100%        |\n| MatchingNet  |         6.21         |       107%        |\n|      TP      |         6.17         |       106%        |\n| TransPropNet |         5.97         |       103%        |\n|  **DUAL-P**  |       **6.14**       |     **106%**      |\n\n\n---\n\n\n**Q3:** *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\n**A3:** Thank you for your thoughtful question. We appreciate the opportunity to elaborate on this aspect of our method. To address your concern, we provide a detailed breakdown of the parameter distribution, as shown in Table R2-J495. \n\nIn total, DUAL introduces **~22 %** additional parameters compared to a parameter-free baseline such as ProtoNet or TP. Specifically, during inference, the repairer is implemented as a single shallow RedNet, while the dual OT is solved just once per episode using lightweight Sinkhorn iterations. \n\nThis carefully designed architecture ensures that the added complexity remains modest, with minimal impact on runtime efficiency. As demonstrated by the inference latency results in Table 5, the overhead introduced is negligible. We believe this balance between enhanced performance and computational efficiency highlights the practicality and scalability of DUAL in addressing the challenges of DSQS.\n\n>Table R2-J495: Comparison of the parameter number of DUAL.\n|     Component      |   Params (M)    | Share of Total |\n|:------------------:|:---------------:|:--------------:|\n| Embedding backbone |      11.18      |      78 %      |\n|     Repairer R     |      3.13       |      21 %      |\n|    Generator G     |      0.07       |     0.5 %      |\n|   Dual‑OT plans    | 0 (solver only) |       –        |\n\n\n[1] Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift"}, {"review_id": "BcbFjolQjn", "reviewer": "Reviewer_q9yF", "summary": "This 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.", "questions": "To 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.", "limitations": "Yes, but they put  the discussion on limitations in Appendix §D.1 instead of the main text.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1. 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.", "was_revised": true, "final_justification": "The rebuttal has helped me better understand the work. I am slightly raising the score to 5.", "rebuttal": "We deeply appreciate your constructive feedback and will carefully incorporate these improvements in the revised manuscript. Thank you for helping us enhance the clarity and quality of our work.\n\n**Q1:** *Insufficient tutorial material on Optimal Transport (OT)*\n\n**A1:** Thank you for highlighting this important concern. We recognize that clearer explanations of Optimal Transport (OT) would greatly aid readers unfamiliar with the concept. In the revised manuscript, we will provide a more comprehensive tutorial on OT, including a detailed introduction to key notations such as $w$ and $\\pi$, as well as their roles in our framework. Specifically, $w$ represents the distance function, while $\\pi$ denotes the optimal transportation plan, which we define more formally in Line 133. Additionally, we will ensure consistency in symbol usage throughout the paper to avoid any confusion. These revisions aim to make the material accessible and self-contained.\n\n--- \n**Q2:** *Difficulty of understanding the overall design.*\n\n**A2:** We appreciate this feedback and agree that providing clearer intuitions and motivations would strengthen the paper. In the revision, we will elaborate on the design rationale behind each component, emphasizing how they jointly address the challenges in DSQS. We will also include illustrative examples and diagrams to visually clarify the connections and flow of the proposed approach.\n\n--- \n**Q3 & Q4:** *Writing inconsistencies, such as typos and unclear notation, and references from less prestigious venues may impact clarity and credibility.*\n\n**A3 & A4:** Thank you for pointing this out. We will carefully revise the manuscript to address grammatical errors and ensure consistent use of terminology and notation throughout. Specifically, the mentioned sentence will be rephrased for clarity, and spelling inconsistencies such as \"Interset\" versus \"inter-set\" will be standardized. These improvements aim to enhance the readability and professionalism of the paper. We acknowledge this concern and will address it by strengthening the discussion of relevant literature. While some key references were chosen based on their technical value rather than venue prestige, we will ensure that our citations include works from established and reputable conferences and journals where applicable."}]

Author rebuttal:

Meta-review: This paper proposes DUAL, a framework for few-shot learning under Dual Support-Query Shift (DSQS), integrating inter-set and intra-set distribution shifts. The idea is well-motivated and supported by both theoretical analysis and extensive experiments. The paper presents technically sound work with clear contributions to the few-shot learning domain. Reviewers agree that the problem formulation is novel and valuable, and the framework shows consistent gains over strong baselines.

While initially there were concerns about clarity, relation to PGADA, and experimental scope, the authors have demonstrated commitment to addressing these issues comprehensively. These were convincing to most reviewers, who raised or maintained positive borderline scores. Therefore, I would recommend acceptance of the paper. For camera-ready, I encourage the authors to:

1. Incorporate all promised writing improvements for clarity
2. Add the visualization analyses discussed in the rebuttal
3. Include the cross-domain FSL discussion and results
Decision: Accept (poster)