Diffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL
Authors: Ruitao Wu
Yifan Zhao
Guangyao Chen
Jia Li
Venue: NeurIPS 2025 poster ()
Keywords: Few-Shot Class-Incremental Learning; Diffusion Model; Reward Alignment
Primary area: deep_learning
TL;DR: This paper introduces Diffusion-Classifier Synergy (DCS), a novel framework where a diffusion model and an FSCIL classifier co-evolve through a dynamic, classifier-derived reward system to achieve state-of-the-art few-shot class-incremental learning.

Abstract: Few-Shot Class-Incremental Learning (FSCIL) challenges models to sequentially learn new classes from minimal examples without forgetting prior knowledge, a task complicated by the stability-plasticity dilemma and data scarcity. Current FSCIL methods often struggle with generalization due to their reliance on limited datasets. While diffusion models offer a path for data augmentation, their direct application can lead to semantic misalignment or ineffective guidance. This paper introduces Diffusion-Classifier Synergy (DCS), a novel framework that establishes a mutual boosting loop between diffusion model and FSCIL classifier. DCS utilizes a reward-aligned learning strategy, where a dynamic, multi-faceted reward function derived from the classifier's state directs the diffusion model. This reward system operates at two levels: the feature level ensures semantic coherence and diversity using prototype-anchored maximum mean discrepancy and dimension-wise variance matching, while the logits level promotes exploratory image generation and enhances inter-class discriminability through confidence recalibration and cross-session confusion-aware mechanisms. This co-evolutionary process, where generated images refine the classifier and an improved classifier state yields better reward signals, demonstrably achieves state-of-the-art performance on FSCIL benchmarks, significantly enhancing both knowledge retention and new class learning.

Diffusion-Classifee Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL
Ruitao Wu
Yifan Zhao!
Guangyao Chen
Jía Li'
State Key Laboratory of Virtual Reality Technology and Systems, SCSE QRI, Beihang University
Zhongggunncun Academy *Peking University (ruitaowu, zhaoyf]@buaa. edu.ccn
Abstract
Few-Shot Class-Incremental Learning (FSCIL) challenges models to sequentially learn new classes from minimal examples without forgetting prior knowledge, a task complicated by the stability-plasticity dilemma and data scarcity. Current FSCIL methods often struggle with generalization due to their reliance on lim- ited datasets. While diffusion models offer path for data augmentation, their direct application can lead to semantic misalignment or ineffective guidance. This paper introduces Diffusion c Synergy (DCS), novel framework that tablishes a mutual boosting loop between diffusion model and FSCIL classifier. DCS utilizes reward-aligned learning strategy, where a dynamic, reward function derived from the classifier's 's state directs the diffusion model. This reward system operates two levels: the feature level ensures semantic coher- ence and diversity using prototype-anchored maximum mean discrepancy and dimension-wise variance matching, while the logits level promotes exploratory image generation and enhances inter-class discriminability through confidence recalibration and cross-session confusion-avee mechanisms. This co-evolutionary process, where generated images refine the classifier and an improved classifier state yields better reward signals, demonstrably achieves fate-of-the-ar perfor- mance on FSCIL benchmarks, significantly enhancing both knowledge retention and new class learning.
Introduction
Huus--nncrmmenal Learning (CIL) [65, 74, 58, 81, 31, 78] endeavors to equip models with the ability to learn new classes sequentially without forgetting previously acquired knowledge, a critical capability for real-world, dynamic environments. Few-Shot Class-nneremental Learning (FSCIL) [53, 52, 77, 50, 76] intensifies this challenge by further constraining that new classes are introduced with only handful of training examples. This exacerbates the notorious stability-plastiiiry dilemma [34] and introduces severe unreliable empirical risk minimization [61] for novel classes due to data scarcity. The core of these issues lies the model's restricted access past data and the limited information available for new concepts [32].
A common thread among mainstream FSCIL approaches is their reliance the limited knowl- edge encapsulated within the initially provided datasets. This inherent limitation hinders their ability to significantly enhance both intra-task generalization (robustness within a learndd class) and inter-task generalization (adaptability and discrimination across incrementally learned classes). The advent of powerful generative models, especially diffusion models [10, 16, 44, 48), of- fers a promising avenue to overcome these data limitations by synthesizing additional train- ing samples. By generating images for old classes, they can facilitate knowledge replay with-
Cooresponning authors.
39th Conference on Neural Information Processing Systems (NeurIPS 2025).

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out explicit storage, and by augmenting new, few-shot classes, they can provide richer train- ing signals. The strong generalization capabiliitiss of pre-trained diffusion models suggest they can introduce diverse and novel variations, potentially improving classifier robustness.
However, the direct application of diffusion
models for data generation in FSCIL is not with-
(a) Unidirectional Knowledge Provision (Conventional)
out significant hurdles. As identified in our analysis (detailed in Section 4.1), two primary problems emerge: (i) Semantic Misalignment and Diversity Deficiency of Generated Images (Figure ((a) (2)). When conditioned solely on class names, vanilla diffusion models tend to generate images with significant semantic de- viations insufficient diversity from the FS- CIL dataset. This results in misrepresentative or overly concentrated distributions, which can in- troduce noise and distort learned decision bound- aries, thereby degrading classifier performance. (ii) Inefficient Feedback for Guiding Image Generation (Figure ((a) 3). Existing genera- tive methods [2. 47] typically lack mechanism for the diffusion model to adapt its output based on the classifier's 'ss current state or learning needs. The generation process often blind to whether the synthesized samples are genuinely benefi- cial, too easy, or too difficult for the classifier, or whether they address critical areas of confusion in the feature space. While million-scale image generation performs well [4], its application is impractical in resource-demanding settings like FSCIL. We therefore prioritize the efficiency associated with generating fewer (< 50) images per class.
Pronist
Dimusion
Xgen
Encodar
Images
Clessifer
Recalibrated Confidence Roward Cross- Bess--nsooon cenfuslon-uuurs Reward
Anchored NMO Reward Bin Wile Variance Matching Reward
Pronista
fenture
Level
Level
Diffution
Encoder
Xgm Imsgga
Clansctlrr
(b) Mutual Boosting Loop (Ours)
AEDO dias baskees third proteypes
caw borpa
featurre serereee
Figure 1: (a) Unidirectional knowledge provision in conventional methods results in inefficient generated im- ages. (b) Our approach mitigates this inefficiency via a combined feature-level and logits-leve reward, facili- tated by mutual boosting loop between the diffusion model and classifier.
To address these challenges, we introduce Diffusion-Classiffer Synergy (DCS), a novel framework that establishes a mutual boosting loop between diffusion model and FSCIL classifier. The core idea is to leverage the Diffusion Alignment as Sampling (DAS) [21] algorithm guided by carefully designed, dynamic reward function. This reward, derived from the classifier's 's state, steers the diffusion model to generate strategically beneficial images. These generated images, in turn, enhance the classifier's training, leading to more refined reward signals, thus creating a co-evolutionary process.
Our DCS framework tackles the aforementioned issues at two distinct levels (Figure 1(b)): (i) At the feature level, where rewards are computed from features extracted by the encoder from images generated by the diffusion model, we introduce a reward design for semantic coherence and diversity (Section 4.2). This mechanism introduces two key rewards. The prototype-anchon discrepancy reward function using MMD to encourage generated image diversity while maintaining consistency with class prototypes. Complementing this, the dimension- wise variance matching reward operates by aligning per-dimension feature variances of generated images with those from limited real data, offering robust approach to match feature spread for new classes. This ensures that generated images are not only semantically anchored to the target class representations but also exhibit rich intra-class variations. (ii) At the logits level, where rewards are computed from classifier outputs for images generated by the diffusion model, we introduce reward design for classifier- aware generation (Section 4.3). This design incorporates a recalibrated confidence reward to encourage the generation of more exploratory and generalized intra-class images, complementing the feature-level diversity reward. Building upon this, a novel cross- coss-sssssonn confusion-awarr reward is proposed, wherein the core idea is to intentionally generate hard samples that target the classifier's weaknesses. This is accomplished by adjusting the weight of classes in the cross-entropy based on the severity of their confusion, with the specific goal of enhancing discriminability between the target new class and its most confusable old classes.
maximum mean
Our contributions can be summarized as follows:
We propose Diffusion-Classiee a mutual boosting loop between the diffusion model and the classifier, leveraging reward- aligned generation via DAS for synergistic co-evolution.
Synergy (DCS), novel FSCIL framework that pioneers

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We design a multi-faceted reward function operating at two distinct levels: at the feature level, it enforces semantic coherence and promotes intra-class diversity; and at the logits level, it guides the generation of exploratory, generalized intra-class images and enhances inter-class diseriminability
We empirically demonstrate state-of-the- performance on challenging FSCIL benchmarks. showcasing significant improvements in both preserving knowledge of old classes and effectively learning new classes with limited data.
Related Works
Class Incremental Learning. In class-incremental learning tasks, models are required continually learn to recognize new classes from a sequential data stream while retaining previously learned knowledge. Data replay-based methods [5. 23, 75, 59, 82] achieve this by storing data from previous tasks (such as image examples or features) or generating images of previously learned classes, allowing the model to revisit past data distributions. Network expansion-
methods [71, 64, 29, 65, 58, 79, 50] dynamically adjust the model's 's architecture or capacity during training to enhance its ability to learn new knowledge. Parameter regularization-bune based methods [69, 70, 25] focus on how the model parameters should dynamically adapt when the network structure remains fixed.
Few-Shot Class-Incremennal Learning. FSCIL aims to achieve continual learning of new knowledge in data-constrained scenarios. Representative works such as [52, 73, 66, 67, 20, 38] focus on dynamic network-centric approaches, maintaining the topological relationships between feature spaces of various categories by dynamically adjusting the network structure. Methods like LIMIT [80, 8, 43] introduce the concept of meta-learning into FSCIL. Feature space-based methods [77, 41, 68, 51, 3, 27, 39, 37] enhance the robustness of the learned feature space by introducing virtual class instances and other means. Recently, many methods [40, 76, 17, 24] have employed pre-trained vision-languagge models (e.g., CLIP) to further enhance the generalization capability of the models.
Diffusion Models for Image Classification. Data augmentation using diffusion model (DM) is an active research area aimed at enhancing image classifier training by synthesizing additional data. Key efforts focus on generating images that are both semantically consistent with class labels and exhibit sufficient richness and diversity to improve classifier performance. Initial studies [4, 35, 28, 13] demonstrated the viability of using fine-tuned DM for large-scale classification improvements, while subsequent research has explored advanced conditioning mechanisms [54] and image editing techniques [19, 62] to enhance semantic control and sample variety. Other approaches investigate leveraging DM features for precise guidance [30] or tailoring generation for specific scenarios like few-shot learning [56] or continual learning [46]. Distinct from the aforementioned approaches, our work focuses on exploring methodologies that dynamically adjust output content based on classifier feedback, with a particular emphasis on enhancing the efficiency of image generation in scenarios characterized by smaller generation scales (e.g., tens of images rather than the conventional millions).
3 Preliminary
Few-Shot Class-Incremental Learning FSCIL involves sequentially learning from a continuous
data stream Dtrain (raain I Each session t introduces training samples (z,, 31) for new set of disjoint classes C³. Crucially, after training on Draain the model is evaluated on all classes encountered thus far: Cs U'_o Cs. FSCIL is characterized by an initial base session (t 0) with ample data, followed by incremental sessions (t > 0) where new classes are introduced with only few examples, typically in N N-way K K-shot format.
Mainstream FSCIL methods tend to adopt an incremental-freet -rozze strategy. An initial model (xx WT fx) (comprising feature extractor f and classifier W) is trained extensively on base session data using a classification loss, e.g., cross-entropy:
Lats (0:2,9) = oo (a (z)
Subsequently, the feature extractor f is frozen. In incremental session s, the classifier weights w for new classes are typically computed as prototypes, which are defined as the average feature embeddings of their K training samples, such that w = * Σ (æc.). The full classifier Wyau
3

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then encompasses weights for all seen classes. Inference at each session employs the Nearest Class Mean (NCM) algorithm [33]. A sample æ is classified by finding the class prototype most similar to its feature embedding (x)): Cr arg max sim (f))) w where sim(.,) is the cosine similarity.
Diffusion Alignment Sampling (DAS) Aligning pre-trained diffusion models with specific rewards while preserving their generative quality and diversity is a key challenge [9, 7, 12, 49, 42, 6, 14, 72]. Fine-tume Fine-tuning can lead to reward vve--optiiizaion, while simpler guidance methods may underperform. [21] proposed DAS, a Iraining-free algorithm to address this. DAS aims to effectively sample from reward-aligned target distribution without explieit model retraining, thereby mitigating over-optimization. The core of DAS is to sample from the target distribution Ptar (æ), which balances reward function r(ær) with fidelity to the pre-trained model Pyre (3:):
(((x) α
Paar (x) 2Ppro( exp
where Z is normalization constant and a is trade-off parameter. To achieve this, DAS employs Sequential Monte Carlo (SMC) [36]. It iteratively guides a set of particles (noisy samples) through the reverse diffusion process. A key feature of DAS is the use of tempered intermediate target distributions (xx)) at each diffusion timestep t:
de P(a))
(x(2)) & (o(x)) exp
(3)
where pr(zz) is the marginal distribution from the pre-trained model, f(x;) is the predicted reward from noisy sample x,, and Xg an annealing schedule (Λę 0 to X = 1). Diverging from the DAS which predominantly utilized rewards such as HPSv2 [63] and TCE [18] to assess image quality, our approach, order to specifically address the FSCIL task, involves inputting the generated image Tgen into the classifier. Rewards are subsequently computed based on the classifier's 's output, and this feedback signal enhanced by combining multiple distinct rewards.
Reward-Aligned Learning via Mutual Boosting Loop for FSCIL
In this section, introduce novel framework, Diffusion-Classifec Synergy (DCS), which leverages a reward-aligned learning paradigm through mutual boosting loop to address the inherent challenges of FSCIL. We first outline the primary issues in FSCIL and present the overarching workflow of our approach. Subsequently, we detail the design of our reward functions at both the feature and logits levels.
4.1 Addressing FSCIL Challenges with Mutual Boosting Loop
FSCIL faces the stability-plasticig dilemma and unreliable empirical risk minimization. Generative models, particularly diffusion models, address this by synthesizing data: generating old class images for knowledge replay mitigates the stability- plasityy-pattiitty issue, while creating new class images augments data for few-shot learning. However, naively integrating diffusion models for data generation in FSCIL presents significant problems, which we will analyze in detail.
Semantic Misalignment and Diversity Deficiency of Generated Images. Synthesizing training images that balance semantic fidelity with background diversity presents a substantial challenge when generative prompts are restricted to class labels. More specifically, we employ Stable Diffusion to generate five images per miniImaeeNet class across various guidance scales, subsequently extracting and classifying features using ImageNet pre-trained ResNet34, Semantic alignment is determined by classification accuracy. contrast, intra-class feature dispersion, an indicator of semantic richness quantified by the average L2 distance of features their class centroid, shows inverse correlation with accuracy as guidance is strengthened (Figure 2 Left). Critically, irrespective of guidance parameters, synthetic data consistently underperforms both classification accuracy and semantic richness compared to ResNet34 on the original minilmageNet testset (baseline).
Inefficient Feedback Loop for Guiding Image Generation. Mainstream dataset augmentation methods treat the diffusion model as a blind teacher, one that is unaware of the student classifier's needs, which hinders adaptive image generation in FSCIL. Specifically, it limits the ability to produce

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samples tailored to the classifier's current capabilities, such as generating images near class decision boundaries. Such fine- gre-ira control is crucial for alleviating semantic confusion between new and old classes. The t-SNE visualization in the right panel of Figure 2 indicates confusing regions between the two classes, which the classifier cannot sufficiently learn from the generated images, leading to easy misclassification of samples in this area.
To overcome these limitations, we propose the Mutual Boosting Loop. Its core idea is to design multiple reward components Rg computed based on the output of the classifier a (with parameters 6) when presented with a generated image z. The diffusion model D is then guided to adjust sampling strategy @ to maximize the sum of these rewards:
092 Avunaa
273
x
arg max Eei
(4)
200
Guidrce
The optimized o" guides image generation D(r; which in turn enhances classifier per-
Figure 2: Left: Trade-off between semantic fidelity and richness with increasing guidance scale. Right: t-SNE visualization features from real (dot) and generated (pentagram) images illustrates the restricted distribution of the latter within the classificr's decision space.
formance:
0" = arg min Ldalout D(2; v)).). (5)
Consequently, the classifier, now optimized with parameters 6", provides more accurate and infor- mative reward signals back to the diffusion model, creating a synergistic co-evolution. The overall process is depicted in Section A. The subsequent sections will elaborate on the construction of this reward mechanism from feature-level (Section 4.2) and logits-level (Section 4.3) perspectives.
4.2 Feature-Level Reward for Semantic Coherence and Diversity
In this section, we detail the process for generating images of a specific class during each session, focusing feature-level reward components. We assume the classifier has undergone initial learning, with its parameters (class prototypes) initialized using the few available samples for new classes. Addressing the first challenge identified in Section 4.1, a straightforward strategy is enforce fidelity and diversity using the Fréchet Inception Distance (FID):
(6) where μa is the mean vector and Σ is the covariance matrix. Subscripts r and g indicate real and generated images, respectively. Despite its utility as an evaluation metric, directly using FID as a reward is impractical because restricted old data access prevents its calculation against the comprehensive dataset, and the limited number of new class samples hinders reliable Σ estimation, leading to unstable scores.
FID |,,- M4 (T, -,(E,,,)),
Drawing inspiration from the principles underlying FID, we propose a more suitable reward mech- anism tailored the constraints of FSCIL. Our goal is achieve similar objectives of semantic coherence (related to mean matching) and feature diversity (related to covariance matching) using components that are robust with limited data. To approximate the goal of matching the overall feature distribution, particularly ensuring that generated images are semantically anchored to their class identity, we introduce the Prototype-Anchored Maximum Mean Discrepancy Reward RAMMMD- MMD is statistical test used to determine if two sets of samples are drawn from the same distribution. Instead of directly comparing means which can be problematic with incomplete data for μu and unstable Bg from few generated samples, MMD allows for more holistic comparison.
When candidate image Zgen is considered for addition to existing set of N 1 generated images I Nc) of class RPAMMD evaluates the quality of the augmented set I(c.N) 7(c.N (Zgen by taking the negative of the MMD:
N k(z, 2,) + N
RPAMMD( (ggen, Ile.N)
1
E k(zi, Ma)
k k( Mt))
(7)
N2
1=1
Constant
Diversity
Consistency
5

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where 2, = [xx;) is the feature of the i-th generated image in Igan Me E RD is the feature prototype for class ye: which is in fact the class weight of the classifier. k(-;-) is a positive definite kernel function, and a, 3 are non-negative hyperparameters.
The first term Diversity effectively measures the internal similarity of the Tgen A lower value for this component (contributing to a higher RPAMMD) is desirable, as it indicates that the generated images are distinct from each other, thereby maximizing diversity. The second term Consistency assesses the collective similarity of the generated images to the class prototype and encourages the Zgen to maintain strong semantic consistency with the target class. The third term is constant that depends only on the class prototype, which can be ignored in the actual computation process. As diffusion models generate images sequentially, the gradient of the REAMMD backpropagated solely to the currently generated image. To mitigate resource wastage from redundant computations, Equation (7) can be updated incrementally. The detailed procedure is provided in Section D.
A key advantage of Reammd is its universality. It is applicable whether the model is generating images for previously learned classes newly introduced classes. As the classifier continuously updates the knowledge acquired for the current class, the prototype μa is also updated correspondingly enabling to provide more accurate feedback the diffusion model in the subsequent session.
Despite the significant utility of RAAMMD. its reliance solely on prototypes when a subset of new-class images is accessible results in the underutilization of valuable information regarding the feature spread. Drawing inspiration from the second term of the FID, which utilizes covariance matrices to reflect the spatial distribution and spread of features, we propose the Dimension-Wai Variance Matching Reward RYM- This component is developed for robustly matching feature variances of novel classes where limited reference data is available and full covariance estimation unstable.
We first estimate the per-dimension variances from the features extracted from real images Z(e) Then, for each new Egen evaluate how it affects the per-dimension variances of the (c.N) candidates that bring these variances closer to the target reference variances:
and reward gen
RVM @gge I(c.N)
ggn
Vreal)
(8)
where gen = Var((f(x))) æ2 E Igen}) is the sample variance of the d-th dimension of features in Igens and o/ is defined analogously.
The rationale for dimension-wise matching, rather than full covariance matching, stems from the instability of covariance matrix estimation in few-shot scenarios. Estimating x D covariance matrix from very few samples (e.g., N < D) can lead to highly inaceurate or singular matrices. Matching variances at the dimensional level alleviates this issue by focusing on more robust univariate statistics, though it forgoes capturing nnfer-dimeniional correlations. Given that this matching process still entails certain sample size requirements, we recommend deferring the incorporation of this term into the overall reward until a sufficient number of images (e.g.. > 5) are generated.
4.3 Logits-Level Rewards for Robust Discrimination Learning
While feature-level rewards ensure semantic integrity and diversity, they do not directly leverage the classifier's decision-making process. To bridge this gap and enable finer-grained control over image generation, we introduce logits-level rewards that incorporate feedback from the current classifier.
fundamental logits-level reward aims to encourage the generation of images that the classifier can correctly assign to the target class ye- This can be initially conceptualized with a standard cross-entropy reward:
cxp(oe((gge)) Σe (2
ReE(Egea: yc) log(p(ye (gea)) log
where (xgggg))) is the logit for the target class ye. p(y. (gg))) the probability assigned by the current classifier to the target class ye for the generated image Tggen However, relying solely on maximizing this basic classification accuracy can lead to the generation of overly simplistic or peaky samples. To address this limitation and encourage the generation of more nuanced and informative samples,

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refine this concept into Recalibrated Confidence Reward with semperature-sealed probability:
Rec (ggn gen: We) (og(p(Ve 20 geni T));
(10)
(F /
Plyel Tggni T)
(11)
En exp gen /
/TT
Po (een) 1/No
(ggge) Thase + Tscale
(12)
1/Ne
where No is the total class count. Thase > 1 provides baseline smoothing, and Teaale > 0 sets the maximum additional temperature. This adaptive temperature adjusts based on the classifier's raw confidence in the target class ye for Fgen- high Pe (eee) increases temperature T, flattening the reward's probability distribution to discourage overly simplistic samples. Conversely, a low Po[yel gen)) keeps near T Tasse minimizing extra smoothing to prevent excessive diffusion. This efficient mechanism dynamically controls classifier feedback smoothness within the logits-level reward, thereby fostering the generation of more challenging and diverse samples.
While the RRC focuses on the confidence of the target class, it does not explicitly address the relationships between different classes. In FSCIL settings, significant challenge arises when feature embeddings of new classes closely overlap with those of previously learned classes. Simply ensuring a high (even if smoothed) probability for the target class Ye might not be sufficient to explicitly create a clear distinction from these confusing old classes. To this end, we introduce the Cross-Session Confusion-Awarr Reward RCSCA, which makes possible to generate harder samples for robust training and refine the classifier's 's understanding of nuanced class differences. To detail how this reward formulated, we first outline how the classifier assesses these inter-class similarities and potential confusions.
The classifier's decision process involves computing the cosine similarity {yel between the features f (Xggn) and the class prototype μac. for each class ye. which is then used to derive the logits:
f gggen) He
$ty Egaa)
(13)
(x |
Based on this similarity, the cosine distance is deos ggg Me) = - (y [gen). which quantifies how dissimilar the generated sample's 's features are from the class center μ To modulate the influence of different classes based on their similarity to the generated sample, we introduce dynamic weights Wye (Zege)) such that the weight for class yı increases as the sample becomes more similar to the pt:
Wy. (I gen)
(14)
1 + y deas ( (Zgon: p4))'
where the scaling factor controls the sensitivity of the weight to the cosine distance. A smaller gann Wt) result in more weight being assigned to the target class y/ for the generated sample. I Leveraging these weights, the Røsca is designed encourage the generator to create samples for a target class Ye that are highly similar to a confusable class Y1 within a set C. This is achieved by defining the reward as the log-probability of classifying &gen as yt instead of ye, using dynamically weighted similarity scores as logits:
ResCA (Igen. Mc) = Σ Euy (I log [ggni Te)),
(15)
yEC
where C can be the set of the top-K most similar prototypes My to Zgen in order to reduce computation.
5
Experiments
5.1
Experimental Setup
Following the benchmark settings of previous methods, we conducted experiments on three datasets, i.e., minilmageNet 45], CUB-200 [57], and CIFAR-100 [22]. The division the datasets aligns with existing methods. Specifically, the CIFAR- 100 and minilmageNee datasets are partitioned into a base session containing 60 classes and incremental sessions containing 40 classes, with each session being an 8-way 5-shot few-shot classification task. The CUB-200 dataset is divided into the base session containing 100 classes and incremental sessions containing 40 classes, with each session being a 10-way 5-shot task.
Datasets
7

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Table 1: Comparison results on minilmageNet dataset. * denotes results from [27].
Accuracy each session (%) f
Methods
Avg
0
3
5
7
8
TOPIC [52]
61.31
50.09
45.17
41.16
37.48
35.52
32.19
29.46
24.42
39.64
CEC [73]
72.00
66.83
62.97
59.43
56.70
53.73
51.19
49.24
47.63
57.75
FACT [77]
72.56
69.63
66.38
62.77
60.60
57.33
54.34
52.16
50.49
60.70
TEEN [60]
73.53
70.55
66.37
63.23
60.53
57.95
55.24
53.44
52.08
61.44
SAVC [50]
81.12
76.14
72.43
68.92
66.48
62.95
59.92
58.39
57.11
67.05
DyCR [38]
73.18
70.16
66.87
63,43
61.18
58.79
55.00
52.87
51.08
61.40
ALFSCIL [26]
81.27
75.97
70.97
66.53
63.46
59.95
56.93
54.81
53.31
64.80
OrCo* [3]
83.22
74.60
71.89
67.65
65.53
62.73
60.33
58.51
57.62
66.90
*DBS [27]
81.40
75.03
71.03
68.00
65.56
61.87
59.04
56.87
55.38
66.02
DCS(Ours)
82.43
77.54
73.00
69.21
67.05
64.44
61.20
60.43
57.99
68.14
Table 2: Comparison results on CUB-200 dataset. denotes results from [27].
Accuracy in each session (%)
Methods
2
3
4
5
10
Avg
TOPIC [52]
68.68
62.49
54.81
49.99
45.25
41.40
38.35
35.36
32.22
28.31
26.28
43.92
CEC
75.85
71.94
68.50
63.50
62.43
58.27
57.73
55.81
54.83
53.52
52.28
61.33
FACT [77]
75.90
73.23
70.84
66.13 13
65.56
62.15
61.74
59.83
58.41
57.89
56.94
64.42
TERN [60]
77.26
76.13
72.81
16
67.77
64.40
63.25
62.29
61.19
60.32
59.31
66.63
SAVC [50]
81.85
77.92
74.95
70.21
69.96
67.02
66.16
65.30
63.84
63.15
62.50
69.35
DyCR [38]
77.50
74.73
71.69
67.01
66.59
63.43
62.66
61.69
60.57
59.69
58.46
65.82
ALFSCIL [26]
79.79
76.53
73.12
69.02
67.62
64.76
63.45
62.32
60.83
60.21
59.30
67.00
OrCo* [3]
74.58
65.99
64.72
63.06
61.79
59.55
59.21
58.46
56.97
$7.99
57.32
61.79
ADBS [27]
79.99
75.89
72.53
68.33
67.92
64.75
64.10
62.93
61.31
60.88
59.65
67.12
DCS(s)us)
83.19
77.32
73.92
70.52
69.79
68.00
65.22
66.59
64.19
64.86
63.40
69.73
Evaluation metrics Session accuracy quantífies model performance within a specific learning session. To evaluate sustained performance and generalization across both previously learned and newly introduced classes, average accuracy is calculated as the mean of all session accuracies from the initial to the current session.
Implementation details For the Diffusion Alignment as Sampling (DAS) algorithm, we utilized the source code made available by the authors. into which our proposed reward mechanisms were integrated. The latest Stable Diffusson 3.5 Medium model [11] served as the foundational diffusion model. To align with the configuration of Stability Al's open-source weights, thereby ensuring the fidelity of the generated images, we adapted the DAS source code for compatibility with Flow Matching Scheduler. Images were generated at resolution of 512 x 512 pixels and subsequently resized match the native resolution of the real dataset before being input to the encoder. During the base session, additional 30 images are generated per class. For new sessions, generate 30 and 10 images for each newly introduced and previously learned class, respectively. For more details, please refer to Section B.
5.2 Comparison with the State of the Art
In this section, compare our proposed DCS with mainstream methods on FSCIL benchmarks. Table I and Table 2 present the accuracy each session and the average accuracy of these methods on the minilmageNet and
respectively.
Compared previous sfate-of-the-art FSCIL methods, our proposed DCS achieves the highest accuracy in each session. Note that the compared methods employ network optimization techniques tailored to the characteristics of FSCIL tasks, including additional self-supervised learning or dis- tribution calibration. In contrast, our DCS achieves performance improvement solely through the generalized knowledge derived from the diffusion model, without any modifications to the baseline classification network. For further results, please refer to Section C.
8

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Table 3: Ablation studies on CIFAR-100 benchmark. Aust Relative improvements of the last sessions compared to the baseline.
Accuracy each session (%) 1
RPAMM
RvM
Rgc
ReSCa
A lest
2
5
7
Baseline
71.97
67.55
62.83
59.73
56.08
52.99
51.55
49.57
73.90
69.05
64.70
60.99
$7.91
55.02
53.82
50.81
+1.24
74.05
69.29
65.06
61.50
58.59
55.63
54.61
51.43
+1.86
75.08
70.77
66.44
62.94
60.61
57.47
57.01
53.07
+3.50
75.96
72.06
67.35
64.38
62.12
60.05
58.99
55.21
+5.64
5.3 Further Analysis
Ablation Study An ablation study was conducted to investigate the contribution of these compo- nents, and Table summarizes their performance on the CIFAR- 100 dataset. To preclude interference from performance disparities in the base session with the experimental results, we employ fixed weights derived from the base session and initiate experiments starting from the first new session. The contribution of REAMMD in aligning semanties yields accuracy improvement of 1. 1.24% compared to the baseline, an advantage further extended to 1.86% by RyM- At the logits level, Rac demonstrates more pronounced contribution to accuracy enhancement compared to the feature-level rewards, boosting performance from 1.86% to 3.50%. This underscores the critical role of feedback from the classifier's decision space in improving the training effectiveness of the generated images. ResCA further highlights the efficacy of generating customized hard samples based on the classifier's 's mastery of the training data, leading to a substantial accuracy increase 5.64%.
The Diffusion Model's Intrinsic Baseline Using pre- trained diffusion models for downstream tasks requires careful consideration of their inherent knowledge's 's im- pact on results. To investigate this, conducted experi- ments by generating varying quantities of training images, employing only foundational CLIP-score reward while
Average New Session Accuracy (96)
imigis per Cass 3.00 slaggn
0.5
maintaining consistency across other parameters. Figure 3 illustrates the average classification accuracy in new ses- sions under different guidance scales. While empirical ev-
idence suggests that larger volume generated samples correlates with improved training efficacy, the substantial resource expenditure associated with such large-scale gen- eration is incongruent with the practical constraints of FS- CIL scenarios. Particularly in low-data regimes (i.e., fewer than 50 generated images per class), the performance of baseline methods fails to reach the level achieved by our proposed approach (indicated by the dashed line). DCS offers a approach that enables achieving performance com- parable to that obtained with larger volume of images, even when using minimal number of generated images under resource-constraine conditions.
0.0
Guidance Scale
Figure 3: Training performance compari- son. DCS versus the vamilla diffusion model generating images at varying quantitics and guidance scales, with the latter underperform- ing at comparable generation scales.
Conclusion
This paper introduced DCS, a novel framework advancing FSCIL by establishing a mutual boosting loop between a diffusion model and classifier through reward-aligned learning. DCS employs feature-level as well logits-level reward system guide the generation of strategically beneficial images, ensuring they are tailored to the classifier's evolving needs. Empirical validation on bench- mark datasets demonstrated DCS's ofte-offthhe-at performance mitigating catastrophic forgetting and effectively learning new classes from minimal data. The study confirmed that aligning generative processes with classifier goals is crucial for continual learning, emphasizing DCS's 's role in using diffusion models to address major FSCIL challenges and enhance adaptability.

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Acknowledgments
This work is partially supported by grants from the National Natural Science Foundation of China (No. 62132002, No. 62202010, and No. 62402015). the Beijing Nova Program (No. 20250484786), the China Postdoctoral Science Foundation under grant (No. 2024M750100), the Postdoctoral Fellowship Program of CPSF under grant (No. G2B20230024), and the Fundamental Research Funds for the Central Universities.
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A Overall Pipeline
DCS aims to enhance the generalization ability of the classifier by constructing bidirectional feedback between the diffusion model and the classifier, while only generating a few images. The workflow of DCS is detailed in Algorithm Compared to the original FSCIL learning strategy, DCS only adds the step of generating new images.
In the base session, we first train the classifier using real data. Since the data in this session is sufficient, we focus on generating more challenging samples with higher ambiguity (by adding Resca based on the feature-level reward) and use them fine-tune the classifier.
In each incremental session, we first initialize the classifier weights We using the limited real data from the new classes (as described in Section 3). Then, we generate additional learning data for both the new and previously learned old classes. For the new classes, prevent overfitting, DCS aims to help the classifier quickly build an understanding of their concepts. Therefore, we only use RRC at the logits level. For the old classes, the focus on their confusion with the new classes, so ResCA is applied, in the base session. Finally, we mix the newly generated data for both new and old classes and fine-tune the classifier.
Algorithm FSCIL with DCS
1: Data: Dbase: Di (K-shot, for f = 1,.... T)
2: Models:
3: Classifier a = (f, W) with parameters (0y, Ow)
4: Diffusion Model D
5: Loss: Lots
6: Rewards: REAMMD, RYM, RRC, ResCa
7: Generated Images Count: Nbase; Nnew, Nold per class
// Base Session
8: Chase t classes in Drase
9: (0,,0w) - arg mino,, fats Cats(o(Dbasee Crase)
10: Xgen base Ø
11: Rbase & RPAMMD + RVM + ReSCA
12: for all class E Chase do
Generate images for the base classes
Xe
gen gen_baae +- Generate(D D, Noacer R)
X Xgen_base +- X ggn__aase base Xe gen_base
(0,,0w) t arg ongming,,er 0w Cets (otsooeD U X Xgenbbase) Chuse)
Fine-tune classifier
16: Ceeen + Chase
// Incremental Session
17:
for all session = = 1...., do
18:
Chewn &- classes in D inn
Co + + t Creen
19:
20:
Iw 1 Ei J(D: (
Initialize Ow for Chew using D p
21:
X Xgen_ine x- Ø
22:
Rnew &- RPAMMD RvM + RgC
23:
for all class e E Chap do
b Generate images for the new classes
24:
Xe genn_nne & Generate D, Nnew, Rnnw, o)
25:
gen inc &- X Xgen_ine inc U X gen_inc
26:
Rold &- REAMMD + RvM + Resca for all class c Ct do
27:
Generate images for the old classes
28:
X gen_lne inc +- Generate(D (D, c, Notd, Rotd, σ)
29:
X Xgen &- Xine U X gen_inc
30:
Di Dirain inz f- Din U X gen inc.
31:
(03, Bg, &- arg mino, Ow
Ch Chew U UC! (a
> Fine-tune classifier
32:
Cseen & Creen U Cr
33:
Evaluate (;;0,, Ow) on Cueen.
22

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B Implementation Details
Diffusion Model As described in Section 5.1, we use Stable Diffusion 3.5 Medium [11] to generate images. For all datasets, the guidance_scaae scale is set to 2.0. The prompt is "a photo of (class name]"; and the negative prompt is "Anime, smooth, close-up, virtual, logo, partial magnification, exquisite". The number of steps for each image is set to 10.
Diffusion Alignment as Sampling (DAS) [21] For datasets, the kl_coeff is set to 0.001, num_particles is set to 16, and tempering ing-gamma is set 0.008.
Hyperparameters In REAMMD. a = 1, 3 = 2, and k = 0 (since this term is constant). In RRC- Thase is set to 2.0, and seente set to 1.0. In ReSCA, is set to 1.0, and only the top-3 old classes y most similar to Ye are considered.
Training Details For the optimizer, SGD is used on all datasets with momentum of 0.9 and weight decay of 0.0005. In addition, we use the cosine annealing strategy to dynamically adjust the learning rate during training. For the CIFAR-100 dataset, the initial learning rate is 0.1 with 50 epochs for the base session, and 0.01 with 5 epochs for the incremental session. For the miniilmageNet dataset, the initial learning rate is 0.1 with 120 epochs for the base session, and 0.05 with 30 epochs for the incremental session. For the CUB-200 dataset, the initial learning rate is 0,002 with 120 epochs for the base session, and 0.0005 with 10 epochs for the incremental session. Following [15, 68, 27]. we employ ResNet-18 18 as the backbone for CUB200, and ResNet- 12 for minilmageNet and CIFAR After the base session, we freeze the shallow layers of ResNet and keep only the last layer for training. with its learning rate individually set to 0.001 times the learning rate of the new class listed above.
Experimental Environment All experiments were performed under Ubuntu 20.04.4 LTS operating system with NVIDIA GeForee RTX 4090 GPU. The experimental code is written in Python 3.8. 19, and the PyTorch (version 1.13.0+culll is used for the deep learning framework. The source code of the diffusion model used in the experiments is from the open source library di diffusers [1] (version 0.32.2).
C
More Results
C.1
Comparison results on the CIFAR-100 Dataset.
In the main paper, we compare different methods in accuracy across all sessions on CUB-200 and minilmageNet. In Table 4, we report the detailed comparison results CIFAR-100 dataset.
Table 4: Comparison results CIFAR-100 dataset. denotes results from [27].
Accuracy in each session (%)
Methods
Avg f
0
3
6
TOPIC [52]
64.10
55.88
47.07
45.16
40.11
36.38
33.96
31.55
29.37
42.62
CEC [73]
73.07
68.88
65.26
61.19
58.09
55.57
53.22
51.34
49.14
59.53
FACT [77]
74.60
72.09
67.56
63.52
61.38
58.36
56.28
54.24
52.10
62.24
TEEN [60]
74.92
72.65
68.74
65.01
62.01
59.29
57.90
54.76
52.64
63.10
SAVC [50]
78.77
73.31
69.31
64.93
61.70
59.25
57.13
55.19
53.12
63.63
DyCR [38]
75.73
73.29
68.71
64.80
62.11
59.25
56.70
54.56
52.24
63.04
ALFSCII [26]
80.75
77.88
72.94
68.79
65.33
62.15
60.02
57.68
55.17
66.75
OrCo* [3]
79.77
63.29
62.39
60.13
58.76
56.56
55.49
54.19
51.12
60.19
* [27]
79.93
75.22
71.11
65.99
62,46
58.38
55.96
53.72
51.15
63.77
DCS(Ours)
81.09
75.96
72.06
67.35
64.38
62.13
60.05
58.99
55.21
66.36
23

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Table 5: Ablation study on generation quality metrics (FID). CLIP Scoref). The vanilla baseline uses the diffusion model without any reward guidance. The key insight is the relative improvement across settings. The degradation FID/CLIP from adding Resca is by design, as this reward's objective is to generate challenging samples near decision boundaries to improve classifier robustness, not to
generate high-fidelity images.
Reward Combination
FID-CF100L
FID-mini,
FID-CUBL
CLIP-CFI00T
CLIP-minit
CLIP-CUB
w/o reward (vanilla)
142.6
135.8
152.1
68.3
69.5
67.1
RPANMD
128.7
120.4
139.3
72.3
73.4
70.6
+ RyM
122.6
113.8
133.1
74.8
76.3
72.5
+ RyM Rge
123.4
114.3
134.3
78.8
80.2
76.1
+ Rys ResCa
133.0
124.2
144.4
72.0
70.2
C.2 Extended Ablation Studies
To complement the ablation study in the main paper, we provide further analyses on the impact of our reward components on generation quality, the necessity of each component, and the generalizability of the reward design across different datasets.
C.2.1 Ablation on Generation Quality Metries
In addition to downstream task performance, an analysis was conducted on the impact our reward components standard generation quality metries, namely the Fréchet Inception Distance (FID) and CLIP Score. Under the data-scarce conditions of FSCIL, the estimation of such metrics from limited number of samples (e.g., 5-30) is subject high variance and may fully represent the true distribution quality. Accordingly, the following evaluation prioritizes the relative improvement across reward configurations benchmarks in large-scale generation literature. Emphasizing relative gains provides a more precise assessment of the targeted effect of each reward component.
absolute scores, the latter not directly comparable to
The ablation study was performed on the first class of the first incremental session for CIFAR-100 mindImageNet, and CUB-200. For each experimental setting, 30 images were generated, and the average scores are reported in Table 5. The results indicate that the fcature-level rewards, RAAMMD and Rym. significantly improve both FID and CLIP scores by promoting semantic consistency and aligning the feature distribution with real data. The inclusion of Rgc leads to the best CLIP scores, as it directly optimizes for classification confidence, pushing generated samples be more semantically pure. Conversely, adding the confusion- aware reward ReSCA degrades these metrics. This result validates our hypothesis: the goal of RescA is generate strategically challenging samples the decision boundary, which are by nature less aligned with the clean data distribution. This demonstrates that reward system can generate only high-fidelity images but also targeted samples tailored to the classifier's s evolving needs.
C.2.2 Ablation on Reward Component Necessity
To demonstrate that all four reward components are necessary for the final performance, we conducted leave-one-out analysis on the CIFAR- 100 dataset. As shown in Table 6, removing any single component from the full DCS framework results in a performance decrease, confirming that all components contribute synergistically.
Table 6: Leave-one-out ablation study CIFAR-100. Removing any component degrades the final average accuracy, demonstrating their synergistic contribution.
Method
Avg. Acc. (%)
DCS (Full Model)
66.36
DCS RAAMDD
65.12
DCS w/o RyM
65.54
DCS w/o Rge
64.07
DCS w/o ReSCA
64.68

---

Table 7: Sequential ablation studies on minilmageNet and CUB-200, showing consistent performance improvement as reward components are added.
minilmageNet
CUB-200
Method
Avg. Acc. (%)
Method
Avg. Acc. (%)
Baseline (w/o reward)
64.03
Baseline (w/o reward)
65.21
RPAMMD
65.28
+ RPAMMD
66.35
ReAMAD RvM
65.57
+ RPaaad + RyM
66.89
RPAMMD RyM + ARc
66.85
+ RIAMMAD + Rym Rgc
67.41
DCS (Full Model)
68.14
DCS (Full Model)
69.73
C.2.3 Generalizability of the Reward Design
To confirm that the effectiveness of reward design is not confined to single dataset, performed the same sequential add-one ablation study on the minilmageNet and CUB-200 datasets. The results, presented in Table 7, show consistent and positive trend in performance gains each reward component is added, demonstrating the general applicability of our approach across diverse benchmarks.
C.3 Qualitative Analysis of Generated Images
Figure displays qualitative comparison, where the first column shows original images from the minilmageNet dataset and the remaining columns show examples generated by method. Guided by text prompts and our multi-faceted reward system, the generated images are not only semantically correct but are also rich in scenic diversity.
D Incremental Computation of Reward Functions
This section details the incremental update rules for the Prototype-Anchored Maximum Mean Discrepancy Reward (RPAMMD) and the Dimension-Win Wise Variance Matching Reward (RvM). These rules allow for efficient computation as the diffusion model generates images sequentially. We denote the feature vector of the k-th generated image Zk.
D.1 Incremental Computation of RPAMMD
Recall the REAMMD formula for set of N generated features I and class prototype pe:
N
N
N
RAMMMD (Zeen I(c.N)) gon
k k(z,, 23)
k(zis pe),
(16)
N
N
Diversity
Consistency
We need to incrementally update term Diversity and term Consistency when new (N)-th feature is generated and added to set of N - existing features.
Let
N-1N-1
$f (N-1)) Diversity
Σ
k(2,, 2,),
(17)
N-1
SC NN-1) Consistency N-1
Σ
k(z,, po).
(18)
(1) Initialization (N 0)
Before any image is generated for the class:
S/00 0,
(19)
25

---

$6 = 0.
(20)
(2) First Sample (N = 1)
When the first image feature z1 is generated:
sp k(21, z1),
(21)
cC k(z1, Mo):
(22)
(3) Subsequent Samples (N >
Assume we have $ S and Sc c(NN-1) for samples, and a new feature NN is generated. The new sum s(N) includes the old sum S(N-1) the self-similarity of the new sample k(=N, æN), and twice the sum of similarities between the new sample and all 1 previous samples (due to symmetry k(zN, zi) + k(zi, 2N) where k is symmetric:
N-1
$ = =SS- k(ZN, 2N) +2
Σ
k(zN 2,).
(23)
The new sum ((N) is the old sum so plus the similarity of the new sample to the prototype:
Sc
s(N) s(N-1) k(ZN,
(24)
With s(N) and s(N) the ReAAMD (I (N)
Me) can be calculated using the Equation (16). To compute EN k(ZN, z.), we need access the feature vectors of the previously accepted images.
D.2 Incremental Computation of RyM
Recall the RyM for candidate nn when added to a set of N generated features, forming an augmented set (N),
(aM Igen
(Fgen.(d) Prcal.[4))
(25)
where Ugeen [d] (ar({ æz E }gen)) }) is the sample variance of the d-th dimension of features Igans and [deat.[d] defined analogously.
The sample variance for set of N values ( 2N] is given by:
N
N
N
N
Var( (2:)) N
2 2)²
Σ
2 No²
Σ 2
N
N
2
N
(26)
For N = 0 and N 1, variance is typically undefined taken as O. We consider N > 2 for variance calculation. To compute (gen,i]] id] incrementally for dimension d, when new feature NN (with d-th component (44 added, need to maintain the sum of values and the of squared values for each dimension d of the generated features.
For each dimension d E {l...., DJ. the sum of feature values in dimension d for N samples is:
N-1
Sum Sum(N-1) (N-1)
Σ
Zi.[d]-
(27)
The sum of squared feature values dimension d for N samples is:
N
SumSq (N-1) (N-1)
(zrree )2.
(28)
26

---

(1) Initialization (N = 0)
Before any image is generated, for each dimension d:
Sumy = 0,
(0)
(29)
SumSq 0,
(30)
Vgen,(d]
(31)
(2) First Sample (N 1)
When the first feature 21 is generated:
Sum (1)
21,(d)
(32)
SumSqd (1)
(21,(4)
(33)
Ngen,[c]] 0.
(34)
(3) Subsequent Samples (N >>
Assume we have Sum (N and SumSq (N- (N-1) for N samples, and a new feature zn (with d-th
component 2N,(4) is generated:
Sum (N) Sum (N-1) [N]
(35)
SumSq (N) SumSq (amsggu--)) (N-1) + (=N.14)
(36)
SumSq (N) (Sum 122) )2
[genn[d]
N
(37)
With ggen,(d [4] calculated for all dimensions using the updated sums, the RyM can be computed.
E
Discussion
E.1
Limitations
The proposed framework's performance is contingent upon access to high-quality, pre-trained dif- fusion models. Furthermore, the efficacy of DCS may be reduced when applied to highly spe- cialized domains poorly represented in the diffusion model's training data, and the framework's 's multi-component reward system and iterative boosting loop introduce complexities in tuning and computational demand.
F.2 Broader impact
The DCS framework is foundational research contribution aimed at improving the adaptability of machine learning models in data-scarce, continual learning settings. As the approach leverages pre-trained diffusion model, the nature and characteristics of the generated images are directly determined by this underlying component. Consequently, the framework inherits any potential socictal impacts, such as fairness considerations or biases, from the foundational model it employs. The safety and security of the generated content are therefore contingent on the specific diffusion model used, and we encourage practitioners to consider the ethical implications of the foundational model selected for any given application.
27

---

Figure 4: Qualitative comparison of real and generated images minilmageNet. The first column displays real images from the dataset. The subsequent columns show diverse and semantically correct images generated by our DCS framework for the corresponding classes.
28


Reviews:
[{"review_id": "LJ7O6YZ6wn", "reviewer": "Reviewer_QbQk", "summary": "The paper focus on few-shot class-incremental learning (FSCIL) and introduces a framework on Diffusion-Classifier Synergy (DCS) which uses the FSCIL classifier to guide the diffusion model to generate image software old classes. The authors propose a reward based learning approach at the feature level and at the logits level to guide the image generation process and aims to address the semantic misalignment and insufficient diversity of images generated by vanilla diffusion models without guidance. The proposed method outperforms competitive baselines on several FSCIL benchmarks.", "questions": "See weaknesses", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths - \n1. The paper is very well-written with clear explanations and analysis for motivations.\n2. The proposed reward based strategy is very intuitive and is very relevant for the FSCIL setting.\n3. The method section is very well formulated and motivated. \n\n\nWeaknesses-\n1. The paper does not ablate the components of the proposed reward system (other than the accuracy). For instance, how close is the distribution of the generated samples to the real distribution (considering the oracle setting assuming access to more images than the few-shot setting)? This is because the generation aims to address the shortage of data and hence is expected to be similar to the real data distribution. The FID and the CLIP score between the generated and real distribution should be checked and ablated with respect to the several components in the reward strategy. The proposed guidance method should be able to improve these metrics over the vanilla diffusion model in order to demonstrate the significance of the reward strategy.\n2. Apart from generating images, recent methods like [a] simply use calibration of new class statistics (mean and covariances) with old class statistics based on similarities and improves very significantly over TEEN [58]. Can the authors compare to the simple calibration method and show that generating images is indeed the right way to go and simple calibration approaches are not that efficient? For instance, a simple comparison of the generated distribution (with the proposed method) and the calibrated distribution [a] could be performed to analyze which is closer to the real distribution.\n\n[a] \"Calibrating higher-order statistics for few-shot class-incremental learning with pre-trained vision transformers.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.", "was_revised": true, "final_justification": "My concerns are addressed in the rebuttal response from authors. I maintain my rating.", "rebuttal": "We sincerely thank you for your careful review and encouraging recognition of our work. We will respond to the questions you have raised in detail below.\n\n> **Q1: Ablation on Generation Quality Metrics.**\n\n**A1:** Thank you for this insightful suggestion. We agree that analyzing the impact of our reward components on generation quality metrics like FID and CLIP score is valuable for a deeper understanding of our method.\n\nWe would like to clarify our initial reasoning for not including these metrics in the main paper. Our work operates in a strict **few-shot** setting, where both the real and generated samples per class are extremely limited (e.g., 5-30 images). Under such data scarcity, metrics like FID and CLIP score are known to be **highly unstable** and can produce misleading results. For instance, FID suffers from unreliable statistical estimations and high variance, while CLIP score on a few samples may not be representative of the true distribution's diversity.\n\nNevertheless, to address the your valid point, we have conducted a new set of ablation studies as requested. We performed these experiments on CIFAR-100, miniImageNet, and CUB-200, focusing on the first class of the first incremental session. For each setting, we generated 30 images and repeated the runs to report the average scores.\n\n**Crucially, we must emphasize two points:**\n\n1.  The absolute scores computed on such a small number of samples will naturally be worse (higher FID, lower CLIP) than those reported in large-scale generation literature. The key insight lies in the **relative improvement** across different reward settings.\n2.  Due to resource constraints during the rebuttal period, this analysis is on a subset of classes. We plan to include a more comprehensive analysis in the final version.\n\nThe results are presented in the table below:\n\n| Reward Combination   | FID-CF100↓ | FID-mini↓ | FID-CUB↓  | CLIP-CF100↑ | CLIP-mini↑ | CLIP-CUB↑ |\n| :------------------- | :--------: | :-------: | :-------: | :---------: | :--------: | :-------: |\n| w/o reward (vanilla) |   142.6    |   135.8   |   152.1   |    68.3     |    69.5    |   67.1    |\n| `R_PAMMD`            |   128.7    |   120.4   |   139.3   |    72.3     |    73.4    |   70.6    |\n| + `R_VM`             | **122.6**  | **113.8** | **133.1** |    74.8     |    76.3    |   72.5    |\n| + `R_VM`+ `R_RC`     |   123.4    |   114.3   |   134.3   |  **78.8**   |  **80.2**  | **76.1**  |\n| + `R_VM`+ `R_CSCA`   |   133.0    |   124.2   |   144.4   |    71.9     |    72.0    |   70.2    |\n\n**Analysis of Results:** \n\nAs the table shows, `R_PAMMD` significantly improves both FID and CLIP scores over the baseline by promoting semantic consistency and diversity. \n\nAdding `R_VM` further enhances these metrics by ensuring the feature spread aligns with real data.\n\nThe inclusion of `R_RC` leads to the **best CLIP scores**. This is expected, as `R_RC` directly optimizes for classification confidence, thus pushing generated samples to be more semantically pure.\n\nMost importantly, using the confusion-aware reward `R_CSCA` degrades both FID and CLIP scores. This result, while seemingly negative, **validates our hypothesis**. The explicit goal of `R_CSCA` is to generate *hard, confusing samples* at the decision boundary, which are by nature less aligned with the true, clean data distribution. This demonstrates that our reward system can strategically generate not just high-fidelity images, but also targeted, challenging samples tailored to the classifier's evolving needs.\n\nWe also wish to clarify that these rewards are not all used simultaneously for the same purpose, as detailed in Algorithm 1 (Appendix):\n\n- For new classes, we use `R_PAMMD` + `R_VM` + `R_RC`. The goal here is to generate high-fidelity, semantically pure samples to quickly and robustly establish the initial concept representation.\n- For old classes, we use `R_PAMMD` + `R_VM` + `R_CSCA`. The goal is to mitigate forgetting and reduce inter-class confusion by intentionally generating 'hard samples' that lie near the decision boundaries of the new classes.\n- The penultimate row of Table 3 in the main paper shows the result of using only the `R_RC`-based strategy for all classes, while the final row demonstrates the performance of our full, specialized approach. \n\nWe hope this detailed analysis addresses your concerns and further highlights the significance of our proposed reward strategy.\n\n\n\n\n\n> **Q2: Comparison with Non-Generative Statistical Calibration Methods.**\n\n**A2:** We will first articulate the fundamental theoretical advantages of a generative approach like DCS over a post-hoc statistical calibration method like [a]. We will then present a new set of experiments directly comparing DCS to [a] on the quality of the learned distributions, as suggested.\n\nWhile Goswami et al. [a] present an elegant and effective method for improving classification, its scope is inherently limited to a post-hoc statistical adjustment. Our generative approach, DCS, operates on a more fundamental level.\n\n*   **Static vs. Dynamic Learning**: The calibration method [a] is **static**. It calculates new class prototypes and covariances and then uses them in a frozen, non-trainable classifier (like NCM or a fixed linear probe). It improves inference but does not and cannot improve the feature extractor or the classifier's decision logic itself. In contrast, DCS is **dynamic**. By generating actual image instances (`x_gen`), we create new training data that can be used to fine-tune the classifier via backpropagation. This allows the model to actively learn a more robust decision boundary, rather than just passively using a better-estimated statistic.\n*   **Extrapolation vs. Interpolation**: The calibration method [a] relies on **interpolation**. It assumes a new class's distribution can be reasonably approximated by a weighted average of base class distributions. This works well when new classes are semantically close to base classes. However, if a new class is truly novel and semantically distant from all base classes, the calibration will be based on irrelevant information, and the resulting statistics will be poor. DCS, powered by a strong diffusion model, has the potential for **true extrapolation**. Guided by our multi-level rewards, it can synthesize novel feature representations that are not a mere blend of old ones, offering a path to generalize to genuinely new visual concepts.\n\nIn summary, calibration [a] is an excellent inference-time enhancement, while DCS is a comprehensive training-time framework that improves the model's core representation and decision-making capabilities.\n\nTo empirically substantiate this, we followed your suggestion to directly compare which learned distribution is closer to the real data.\n\n*   **Experimental Setup:** We calculated the FID to measure the similarity between the real test data and the data distributions produced by both methods. The experiment was conducted on the 10 new classes from the first incremental session of the CUB-200 dataset.\n    *   For our **DCS** method, we generated images for each new class and calculated the FID directly between the paths of these generated images and the real images using a standard FID implementation (e.g., PyTorch-FID with InceptionV3 features).\n    *   For **method [a]**, since it operates purely in the feature space and does not generate images, a direct image-based FID is not possible. To perform a fair comparison, we sampled feature vectors from its calibrated Gaussian distribution and compared them to the real features.\n\n*   **Results:** The average FID scores across the 10 new classes are:\n    *   **Method [a] (C-RanPAC, Feature-FID): 305.68**\n    *   **Our Method (DCS, Image-FID): 197.2**\n\nThe significantly lower FID score for DCS provides quantitative evidence that our reward-aligned generative process produces a distribution that is a more faithful approximation of the true data distribution.\n\nWe believe this result, combined with our conceptual arguments, supports the value of our generative approach. However, we also wish to be transparent about the context of this experiment. As noted in A1, evaluating generative quality in the extreme data scarcity of FSCIL is challenging, and any single metric like FID should be interpreted with caution. We believe a deeper analysis of these two distinct paradigms for FSCIL is a promising direction for future research.\n\n\n[a] \"Calibrating higher-order statistics for few-shot class-incremental learning with pre-trained vision transformers.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024."}, {"review_id": "fnLcirzgH9", "reviewer": "Reviewer_Dm5X", "summary": "This paper proposes Diffusion-Classifier Synergy (DCS), a reward-aligned framework for Few-Shot Class-Incremental Learning (FSCIL). DCS establishes a sampling loop between a pre-trained diffusion model and a frozen classifier, where the diffusion model generates class-conditional samples guided by classifier-derived rewards. The reward system comprises four components operating at feature and logits levels: prototype-anchored MMD (RPAMMD), dimension-wise variance matching (RVM), recalibrated confidence (RRC), and cross-session confusion-aware rewards (RCSCA). These rewards are integrated into a diffusion sampling framework (based on DAS) to produce informative, diverse, and semantically aligned images. The method achieves strong performance on CIFAR-100, miniImageNet, and CUB-200 benchmarks, using a small number of synthetic samples per class.", "questions": "1.\tTo properly isolate the contribution of your framework, can you provide a comparison against a baseline that also uses Stable Diffusion 3.5 but employs a simpler generation strategy (e.g., vanilla data augmentation without reward guidance, or a simpler guidance mechanism)?\n2.\tHave you tested the reward mechanism on alternative generative models (e.g., GANs or VAEs)? Would the same guidance strategy work in a non-diffusion setting?\n3.\tHave you investigated the potential risks of the RCSCA strategy, such as polluting the inter-class boundary and leading to poorer generalization? Do you have any mechanisms to prevent or monitor such negative effects?\n4.\tIs it feasible to apply DCS with a different encoder? How sensitive is the reward feedback to the encoder quality?", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths：\n1.\tThe paper accurately identifies two key challenges in using generative augmentation for FSCIL: semantic misalignment of generated samples and their ineffective utility for classifier training , proposing a structured, reward-based solution that addresses both.\n2.\tThe framework is training-free on the generator side and operates under realistic resource constraints (generating a small number of samples per class) , which makes it practically appealing.\n3.\tThe paper is exceptionally well-written. The motivation is strong, the methodology is well-structured, and the diagrams (e.g., Figure 1) are highly illustrative.\n\nWeaknesses:\n1.\tThe authors assert that “DCS achieves the highest accuracy in each session”. However, in the comparison on the CIFAR-100 dataset (Table 1), the accuracy of DCS is partly and averagely lower than that of the compared methods ALFSCIL. The more objective academic practice is to highlight the best-performing result in each column, regardless of which method achieved it.\n2.\tThe DCS framework's construction relies on an intricate combination of four different reward functions and a guided sampling algorithm (DAS). The authors do not sufficiently justify why this specific combination of four rewards is necessary, nor do they discuss potential redundancies or conflicts between them. For instance, how is the optimization balanced when RCSCA aims to generate confusing samples while other rewards aim for semantic consistency? \n3.\tThe analysis of the reward components is confined almost entirely to the CIFAR-100 dataset . This lack of testing on other benchmarks like miniImageNet or CUB-200 means the generality of the reward design is unverified. \n4.\tThe methodology of the ablation study in Table 3 is sequential “add-one” approach, which fails to prove that all four components are necessary for the final model's performance. The “leave-one-out” analysis(each component is individually removed from the full four-reward system) is required to establish true necessity. It is plausible that a simpler combination of rewards (e.g., only the two powerful logits-level rewards, RRC and RCSCA) might offer a better or more efficient performance-to-complexity trade-off.\n5.\tNo comparisons are provided against other generative frameworks (e.g., GANs or VAEs), nor against non-reward-guided diffusion generation. This weakens the attribution of gains to the proposed strategy.", "was_revised": true, "final_justification": "The authors address my concern well during the rebuttal phase. Therefore, I give a positive rating.", "rebuttal": "We sincerely thank you for your careful review and encouraging recognition of our work. We will respond to the questions you have raised in detail below.\n\n\n\n> **Q1: Inaccurate phrasing of performance claims and suboptimal table formatting.**\n\n**A1:** We will carefully revise the manuscript to use more precise and nuanced language when describing our model's performance. We will adopt your suggestion to **bold the best-performing result in each column** of our comparison tables.\n\n\n\n> **Q2: Necessity and Interplay of the Multi-Component Reward.**\n\n**A2:** Our design is a deliberate, hierarchical system, not an arbitrary mix, and we will clarify this in the revision. The core idea is that different rewards are deployed to solve different, specific problems.\n\n1.  **Foundational Rewards:** The feature-level rewards, $R_{PAMMD}$ and $R_{VM}$, work in tandem to establish a necessary semantic foundation. As shown in Table 3 in the main paper, adding $R_{PAMMD}$ alone improves the final session accuracy by +1.24% over the baseline. Incorporating $R_{VM}$ further boosts this relative improvement to +1.86%, demonstrating their combined utility in ensuring generated images possess core class coherence and rich intra-class diversity.\n\n2.  **Targeted, Mutually Exclusive Rewards:** Our framework employs a task-dependent strategy based on the learning objective, as detailed in Algorithm 1 (Appendix):\n    -   **For New Classes:** The goal is to build a robust initial understanding. We use $R_{PAMMD}$ + $R_{VM}$ + $R_{RC}$ to generate high-confidence examples that firmly establish the new concept for the classifier.\n    -   **For Old Classes:** The challenge is preventing confusion with new classes. Here, we use $R_{PAMMD}$ + $R_{VM}$ + $R_{CSCA}$ to generate strategically challenging \"hard\" samples that specifically probe and strengthen the decision boundaries.\n    -   The data in Table 3 in the main paper validates this specialized strategy. The penultimate row, which uses only the $R_{RC}$-based strategy for all classes ($R_{PAMMD}$ + $R_{VM}$ + $R_{RC}$), achieves a relative improvement of +3.50% in the final session. However, the final row, representing our full approach that also incorporates $R_{CSCA}$ for old classes, demonstrates a significantly higher final-session improvement of +5.64%.\n3.  For a more detailed analysis of the conflict associated with $R_{CSCA}$, we have provided an in-depth discussion in our response to Q3.\n\n\n\n> **Q3: On the potential risks of the confusion-seeking R_CSCA and its balance with consistency rewards.**\n\n**A3:** Thank you for raising this important point regarding the balance and stability of our reward system. The framework manages this balance primarily through two key mechanisms.\n\nFirst, the influence of `R_CSCA` is **explicitly controlled** by the scaling factor `γ` in Equation (14). This parameter governs the sensitivity of the dynamic weights to the cosine distance, allowing us to regulate the degree of confusion in the generated samples. This provides a clear mechanism to balance the objective of `R_CSCA` with the feature-level consistency rewards. We will clarify in our revision that this parameter can also be made learnable to enable adaptive balancing.\n\nSecond, the diffusion model's generation process is always conditioned on an **explicit text prompt** describing the target class. This textual conditioning provides a persistent semantic guide throughout the denoising steps, ensuring that the final output does not deviate significantly from the target class's core concept, even as `R_CSCA` encourages exploration near decision boundaries.\n\nWe believe this is a very important direction, and we are keen to explore the precise impact of these hard-sample generation strategies on the classifier's decision boundary as a significant part of our future work.\n\n\n\n> **Q4: Inadequate Ablation Study Methodology and Component Necessity.**\n\n**A4:** We agree that a more comprehensive ablation study is crucial for demonstrating the necessity of each reward component. To address this, we have conducted a **\"leave-one-out\"** analysis on the CIFAR-100 dataset. Regarding the combination of rewards, as pointed out in Q2, `R_RC` and `R_CSCA` will not be used at the same time, so we choose the combination of `R_PAMMD` + `R_RC`.\n\nThe results are presented in the table below:\n\n| (Table 1) Method        | Avg. Acc. |\n| :---------------------- | :-------: |\n| **DCS (Full Model)**    | **66.36** |\n| DCS w/o `R_PAMMD`       |   65.12   |\n| DCS w/o `R_VM`          |   65.54   |\n| DCS w/o `R_RC`          |   64.07   |\n| DCS w/o `R_CSCA`        |   64.68   |\n| `R_PAMMD` + `R_RC` Only |   64.08   |\n\nAs demonstrated in Table 1, removing any single component results in a performance decrease, confirming that all four components contribute synergistically to the final performance of DCS.\n\n\n\n\n\n> **Q5: Limited Generality Verification of the Reward Design.**\n\n**A5:** We appreciate you raising this point regarding the generalizability of our reward design. We would like to emphasize that the performance of our full DCS model across three diverse benchmarks is in itself the most direct and powerful evidence of our method's generality. We understand, however, that an explicit component-wise analysis on each dataset would further strengthen this claim. To make this point more explicit and fully address your query, we are happy to provide the sequential ablation studies for *mini*ImageNet and CUB-200 below.\n\n| (Table 2: miniImageNet) Method | Avg. Acc. |\n| :----------------------------- | :-------: |\n| Baseline (w/o reward)          |   64.03   |\n| + `R_PAMMD`                    |   65.28   |\n| + `R_PAMMD` + `R_VM`           |   65.57   |\n| + `R_PAMMD` + `R_VM` + `R_RC`  |   66.85   |\n| **DCS (Full Model)**           | **68.14** |\n\n\n\n| (Table 3: CUB-200) Method     | Avg. Acc. |\n| :---------------------------- | :-------: |\n| Baseline  (w/o reward)        |   65.21   |\n| + `R_PAMMD`                   |   66.35   |\n| + `R_PAMMD` + `R_VM`          |   66.89   |\n| + `R_PAMMD` + `R_VM` + `R_RC` |   67.41   |\n| **DCS (Full Model)**          | **69.73** |\n\nAs these tables clearly show, the performance gains from each reward component follow a consistent and positive trend across all three datasets. \n\n\n\n\n\n\n\n> **Q6: Applicability of the reward mechanism to alternative generative models (e.g., GANs, VAEs).**\n\n**A6:** Our primary consideration for using diffusion models over other generative models is their significant, principled advantages for **adaptation to few-shot sets**. The adversarial equilibrium between a GAN's generator and discriminator is delicate. Fine-tuning on a small dataset can easily disrupt this balance, leading to **mode collapse**. By contrast, diffusion models are architecturally more resilient and offer more flexible adaptation pathways.\n\nFurthermore, the core mechanism of Diffusion Alignment as Sampling (DAS) is fundamentally and inextricably linked to the iterative reverse process that is the unique hallmark of diffusion models. DAS operates by intervening within a Markovian sequence of noisy latent states ($x_T \\rightarrow x_{T-1} \\rightarrow ... \\rightarrow x_0$). It guides this trajectory at each step by resampling candidate states based on a reward function, a process enabled by the step-wise nature of denoising.\n\nThis locus of intervention is architecturally absent in GAN and VAE frameworks:\n\n* **GANs** employ a generator that performs a **single, deterministic mapping** from a latent vector $z$ to a final sample $x$. There is no intermediate, sequential state between $z$ and $x$ in which to apply iterative guidance at test-time. Any \"guidance\" would have to involve either optimizing the input latent $z$, a different and more constrained problem, or altering the generator's weights, which constitutes re-training, not test-time alignment.\n* **VAEs** similarly use a decoder to perform a **one-shot mapping** from a latent code to a sample. The generation is not a sequential refinement process that can be guided step-by-step.\n\nTherefore, the DAS algorithm is **conceptually incompatible** with the architectural paradigm of GANs and VAEs.\n\n\n\n\n\n> **Q7: On the comparison against non-reward-guided generation.**\n\n**A7:** The requested baseline is, in fact, the **'Baseline'** row in our ablation study (**Table 3** in the main paper). This baseline utilizes the same diffusion model for augmentation but omits our proposed reward guidance. As the data shows, the baseline's accuracy degrades to 49.57% in the final session. In contrast, our full method, which includes all reward guidance components achieves a final accuracy of 55.21%. We acknowledge this was not explicitly stated and will clarify its role in the revised manuscript. Thank you for helping us improve the paper's clarity.\n\n\n\n\n\n> **Q8: Regarding the feasibility and sensitivity of the encoder.**\n\n**A8:** Yes, our DCS framework is designed to be **encoder-agnostic**. It is compatible with any architecture that provides feature embeddings and classification logits, as these are the direct inputs to our reward functions.\n\nRegarding sensitivity, our framework **demonstrates notable robustness**. This stems from two key factors. First, in the standard FSCIL setting, the encoder is well-initialized during the base session where data is ample, ensuring high-quality initial reward signals. Second, the diffusion model's generation is also guided by explicit textual prompts. This textual conditioning acts as a strong semantic anchor, ensuring the generated images maintain class identity even if the classifier's feedback is imperfect. This dual-guidance ensures the co-evolutionary process remains on a positive trajectory, making our framework less sensitive to the encoder's initial quality than one might expect."}, {"review_id": "fn2KBL3V5b", "reviewer": "Reviewer_eG2t", "summary": "This paper presents a reward-alignment-based method for Few-Shot Class-Incremental Learning (FSCIL).\nDifferent from previous works that operate on representative spaces or use meta-learning, this work suggests using a diffusion model to generate data for few-shot learning.\nTo alleviate the fidelity-diversity challenge and Inefficient feedback from the classifier, the paper presents a two-level (i.e., feature- and logits-level) reward system.\nThe authors show experiments for the effectiveness of the proposed methods.", "questions": "Please see the weakness above.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. New insights might be provided by the generative-based methods for FSCIL.\n2. The design of rewards is with strong motivation.\n3. The experiment results shown are with convincing improvement.\n\nWeaknesses:\n1. The first concern is the technical contribution of this work. It is great to see the incorporation of rewards into diffusion models during FSCIL. However, the proposed method is heavily based on Diffusion Alignment as Sampling (DAS). \n2. Lack of training time analysis during training, since the proposed method needs the diffusion model for generating images, it should be clarified if this significantly extends the training process.\n3. I think it lacks detailed comparisons of the proposed method and previous generative-based incremental learning methods like [1-2].\n\nRef:\n\n[1]Gao, Rui, and Weiwei Liu. \"Ddgr: Continual learning with deep diffusion-based generative replay.\" International Conference on Machine Learning. PMLR, 2023.\n\n[2]Meng, Zichong, et al. \"Diffclass: Diffusion-based class incremental learning.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024.", "was_revised": true, "final_justification": "Thanks for the rebuttal provided by the authors. I think most of my concerns are addressed, so I will keep my original positive score unchanged.", "rebuttal": "We sincerely thank you for your careful review and encouraging recognition of our work. We will respond to the questions you have raised in detail below.\n\n\n\n> **Q1: Clarity on Technical Contribution Beyond DAS.**\n\n**A1:** Thank you for your positive feedback. This is an insightful question, and we appreciate the opportunity to clarify the core novelty of our work.\n\nWe agree that our method is built upon an alignment algorithm, for which we chose Diffusion Alignment as Sampling (DAS). As we state in the Preliminary section, having an effective, training-free method to align the diffusion model with a reward signal is a foundational premise for our framework. Indeed, our approach is not strictly limited to DAS. Any similar technique that achieves this goal, such as those proposed in [a] or [b], could be integrated into our system.\n\nThe central technical contribution of our work, Diffusion-Classifier Synergy (DCS), is the design of the **mutual boosting loop** itself. Our focus is on how to leverage the generated data distribution to create a controllable, joint co-evolutionary framework for a downstream task. The classifier's evolving state provides a rich, multi-faceted reward signal that guides the diffusion model to generate strategically beneficial samples, which in turn are used to refine the classifier's understanding. This concept of co-evolution is particularly crucial in extreme scenarios like FSCIL, where data is scarce and every new sample must be maximally informative.\n\nIn the future, we will certainly explore using novel distribution metrics and target alignment algorithms to optimize the sampling step. Thank you again for your valuable suggestion, which points toward an important direction for the future improvement of our work.\n\n**Ref:**\n\n[a] Cheng, Min et al. \"Diffusion Blend: Inference-Time Multi-Preference Alignment for Diffusion Models.\"\n\n[b] Li, Xiner et al. \"Dynamic Search for Inference-Time Alignment in Diffusion Models.\"\n\n\n> **Q2: Lack of analysis of training time.**\n\n**A2:** The primary computational overhead of our method is concentrated in the image generation phase that precedes the training of each incremental session. It is important to note that our approach, by leveraging the training-free DAS algorithm, does not require any fine-tuning of the diffusion model itself.\n\nIn our experiments, the highly parallelizable image generation for a typical incremental session takes **5-10 minutes** (30 images for each new class and 5-10 for each old class). The subsequent classifier training on this augmented data is very fast, taking **less than a minute** per session. Given the limited number of sessions in standard FSCIL benchmarks, we believe this time overhead is a reasonable and acceptable trade-off for the performance gains achieved.\n\nCrucially, our method adds **no overhead at inference time**. The diffusion model is discarded after training, and the final classifier is a standard ResNet. Therefore, its architecture, parameter count, and inference speed are **identical to the baseline**.\n\nFinally, the computational cost is flexible and can be further optimized by generating fewer samples, adjusting the DAS configuration, or employing a more lightweight diffusion model, allowing users to balance performance with available resources. We will add a detailed analysis of this in the final version of the paper.\n\n\n\n\n> **Q3: A detailed comparison between the proposed method and previous generative-based incremental learning methods.**\n\n**A3:** Thank you for your insightful suggestion. We agree that a detailed comparison with related generative-based methods would strengthen our paper, and we will incorporate this discussion into our final version. We hope this clarifies the distinctions and highlights the specific contributions of DCS in the challenging FSCIL context.\n\nWe would first like to clarify a key distinction in the problem settings. The cited works, DDGR and DiffClass, primarily address the **general Class-Incremental Learning (CIL)** problem. In CIL, while the data for new tasks may be limited, it is relatively sufficient, and the main goal is to mitigate catastrophic forgetting. Our work focuses on the more challenging **Few-Shot Class-Incremental Learning (FSCIL)** setting, where new classes are introduced with extremely few samples (e.g., 5-shot). This not only exacerbates forgetting but also makes it incredibly difficult for the model to learn robust new concepts.\n\nDue to this fundamental difference in task settings and evaluation protocols, a direct comparison of experimental results is not feasible. However, a comparison of the design philosophies is indeed valuable and reveals the unique contributions of our DCS framework.\n\n---\n\n##### **Comparison with DDGR**\n\n*   **Interaction Mechanism**\n    *   **DDGR** employs a direct, gradient-based intervention. It calculates the classifier loss's gradient with respect to the noisy image at each sampling step and injects this gradient to steer the generation. This is a form of micro-level, direct manipulation.\n    *   **DCS** utilizes a more flexible Reward-Aligned Learning paradigm. We design a multi-faceted reward function that assesses the strategic value of a generated sample based on the classifier's state. Then, we use the DAS algorithm to optimize the sampling process to maximize this reward. This is a macro-level, goal-oriented guidance.\n*   **Guidance Signal Complexity**\n    This is the core difference and our key innovation.\n    *   **DDGR's** guidance signal has a singular objective: improving the classification probability of the generated sample.\n    *   **DCS** designs a sophisticated, two-level reward system that goes beyond simple accuracy.\n*   **Strategic Goal**\n    *   **DDGR** primarily aims for high-quality replay of past data for rehearsal.\n    *   **DCS** has a broader strategic goal. It not only replays old data but also performs crucial data augmentation for the new few-shot classes and, as mentioned, generates targeted hard samples to enhance the classifier's discriminative power. It provides data for \"review,\" \"preview,\" and \"remedial lessons.\"\n\n---\n\n##### **Comparison with DiffClass**\n\n* **Core Paradigm**\n\n  *   **DiffClass** adopts a \"preventative alignment\" approach. At the start of each incremental step, it fine-tunes the diffusion model to align the generated data distribution with real and historical data. **The information flow is one-way**: from the generator to the classifier.\n  *   **DCS** establishes a \"co-evolutionary synergy.\" It creates a continuous, bidirectional feedback loop where the classifier's real-time state dynamically guides the diffusion model's generation, leading to a synergistic improvement of both models.\n\n* **Generation Mechanism**\n\n  This represents a fundamental distinction in the technical implementation of the two methods.\n\n  *   **DiffClass**: Employs a **model fine-tuning** approach. At each incremental stage, it leverages LoRA and theMulti-Distribution Matching loss function to actively train and modify the weights of the diffusion model. This process involves gradient computation and model parameter updates.\n  *   **DCS**: Adopts a **training-free guidance** methodology.  Throughout the entire generation process, the weights of the diffusion model remain fixed and unchanged.\n\n* **Guidance Signal Complexity**\n\n  *   **DiffClass** relies on a Multi-Distribution Matching loss, which is a macro-level control that aligns the overall statistics of data distributions.\n  *   **DCS's** reward system offers precision guidance. It operates at a granular level, considering not just distribution alignment but the strategic utility of each sample."}, {"review_id": "RzEsQUFv2W", "reviewer": "Reviewer_doaJ", "summary": "This paper introduces Diffusion-Classifier Synergy (DCS) that tackles data scarcity and the stability-plasticity dilemma for FSCIL.  DCS establishes a co-evolutionary, mutual boosting loop between a diffusion model and an FSCIL classifier. The core of the framework is a reward-aligned learning strategy where a multi-faceted reward function guides the diffusion model to generate strategically beneficial images. This reward system operates at feature and logit levels. By having the improved classifier provide more refined reward signals back to the diffusion model, DCS achieves state-of-the-art performance on FSCIL benchmarks.", "questions": "Please see the weaknesses.", "limitations": "Yes, in the supplementary material.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength:\n\n- The concept of a \"mutual boosting loop\" is departure from conventional methods that use generative models as a one-way data provider. \n- The paper designs a multi-faceted reward function that operates at both the feature and logits levels.\n- The framework is designed to solve the key challenges of using diffusion models in FSCIL: semantic misalignment, lack of diversity, and inefficient, non-adaptive feedback.  The ablation study demonstrates that each proposed reward component contributes positively to the final performance.\n\n\nWeaknesses:\n\n- The method's superiority is not consistent across all datasets presented. For example, while it shows a good result on miniImageNet, its average accuracy on CIFAR-100 is slightly below that of a previously published method, ALFSCIL (66.75%). Also, the improvement on CUB is marginal. The paper does not analyze or explain this discrepancy between datasets.\n\n- he framework introduces significant computational overhead compared to traditional FSCIL methods. It requires integrating a large, pre-trained diffusion model (Stable Diffusion 3.5 Medium) and running an iterative, reward-based sampling algorithm (DAS).  The paper lacks a direct comparison of training time or computational cost against other FSCIL baselines, which is a critical factor for evaluating practical applicability.\n\n- The experiments use generated images at a 512x512 resolution, which are then resized.  This is a major deviation from standard FSCIL protocols, where datasets like CIFAR-100 (32x32) and miniImageNet (84x84) are used at their native, much smaller resolutions. This use of a high-resolution generator could provide an advantage not available to compared methods and affects the fairness of the comparison.\n\n- While the paper is built on generating \"strategically beneficial images,\" it does not provide any visual examples of these images or analysis of how the generated images differ from the real images.", "was_revised": true, "final_justification": "The authors have adequately addressed my concerns. Therefore, I decided to maintain the score.", "rebuttal": "We sincerely thank you for your careful review and encouraging recognition of our work. We will respond to the questions you have raised in detail below.\n\n\n\n> **Q1: Inconsistent performance superiority across all datasets.**\n\n**A1:** Thank you for this insightful comment. We appreciate the opportunity to provide a more nuanced analysis of our model's performance.\n\nWe agree that a holistic evaluation across multiple metrics is crucial for FSCIL. While average accuracy provides a general overview, we argue that **final session accuracy** and forgetting rate are more critical metrics for evaluating true continual learning, as they measure a model's long-term viability. \n\nOn CIFAR-100, our method demonstrates superior knowledge retention. From session 1 to 8, ALFSCIL's accuracy drops by 22.71% (from 77.88% to 55.17%), whereas DCS's accuracy drops by only 20.75% (from 75.96% to 55.21%). Crucially, our performance in the final session is higher than ALFSCIL's, highlighting our model's enhanced ability to both learn new concepts and mitigate catastrophic forgetting.\n\nFurthermore, on the miniImageNet benchmark, DCS significantly outperforms ALFSCIL across the board: our average accuracy is 3.34% higher (68.14% vs. 64.80%), and our final session accuracy is 4.68% higher (57.99% vs. 53.31%). On the fine-grained CUB-200 dataset, while SAVC's average accuracy (69.35%) is the closest to ours (69.73%), DCS achieves a higher final session accuracy (63.40% vs. SAVC's 62.50%) and shows a smaller accuracy drop from the first to the last incremental session, indicating better long-term learning.\n\n**The performance variation stems from dataset characteristics.** CUB-200's fine-grained nature presents a unique challenge due to high inter-class similarity. We see it as a strength that DCS, with a unified framework and without dataset-specific hyperparameter tuning, achieves consistent and significant improvements on diverse datasets. This demonstrates the generalizability of our core concept. However, we agree that exploring reward designs tailored to the semantic distributions of fine-grained datasets is a valuable direction for future research.\n\nWe hope this clarifies the significant advantages of our approach. Thank you again for your valuable feedback.\n\n\n\n> **Q2: Concerns on Computational Efficiency.**\n\n**A2:** The primary computational overhead of our method is concentrated in the image generation phase that precedes the training of each incremental session. It is important to note that our approach, by leveraging the training-free DAS algorithm, does not require any fine-tuning of the diffusion model itself.\n\nIn our experiments, the highly parallelizable image generation for a typical incremental session takes **5-10 minutes** (30 images for each new class and 5-10 for each old class). The subsequent classifier training on this augmented data is very fast, taking **less than a minute** per session. Given the limited number of sessions in standard FSCIL benchmarks, we believe this time overhead is a reasonable and acceptable trade-off for the performance gains achieved.\n\nCrucially, our method adds **no overhead at inference time**. The diffusion model is discarded after training, and the final classifier is a standard ResNet. Therefore, its architecture, parameter count, and inference speed are **identical to the baseline**.\n\nFinally, the computational cost is flexible and can be further optimized by generating fewer samples, adjusting the DAS configuration, or employing a more lightweight diffusion model, allowing users to balance performance with available resources. We will add a detailed analysis of this in the final version of the paper.\n\n\n\n\n\n\n\n> **Q3: Concerns regarding the fairness of comparison due to the use of high-resolution image generation.**\n\n**A3:** We would like to respectfully clarify a potential misunderstanding. While the images were initially generated at a $512 \\times 512$ resolution, they were **always resized down to the native resolution of the respective dataset** (e.g., $32 \\times 32$ for CIFAR-100) using `torchvision.transforms.Resize` **before** being input to the classifier. \n\nCrucially, this downscaling process is inherently lossy; the resulting low-resolution image does not contain more information than an original image of the same size and is simply a synthetic sample that conforms to the standard data format. Therefore, the model was not trained on high-resolution images in any form, **ensuring no unfair informational advantage** was conferred.\n\nThe decision to generate at a higher resolution was a practical necessity. The performance of the Stable Diffusion model we employed is optimized for specific resolutions. This is because its core model was pre-trained on images of larger dimensions, such as $512 \\times 512$, $1024 \\times 1024$, or even higher. At the very low native resolutions of the FSCIL datasets, the model is **unable to generate** coherent, semantically meaningful images, often producing only noise. Our approach allows us to use this state-of-the-art generative tool effectively, while still maintaining a fair and standardized evaluation protocol for the classifier.\n\n\n\n\n\n> **Q4: Lack of visual examples and analysis for the images.**\n\n**A4:** We agree that visualizing the generated images is crucial for a complete understanding of our method's mechanics. We will certainly incorporate a dedicated section with visual examples and a comparative analysis in the final version of the paper.\n\nDue to the format limitations of this rebuttal, we are unable to display images directly. Instead, we offer the following textual description of their key characteristics:\n\n*   **Semantic Correctness and Style Diversity:** Guided by both text prompts and our multi-faceted reward system, the generated images are not only semantically correct but also rich in scenic diversity. Since our diffusion model is guided by the classifier’s state rather than the original dataset's images, the generated samples exhibit a broader range of styles than the source data. This process, constrained by our logits-level rewards, ensures the classifier can correctly identify these novel variations, thereby significantly enhancing its generalization.\n\n*   **Subject Amplification:** A key difference from real images lies in the portrayal of the subject. Our method provides a clear semantic target, prompting the diffusion model to amplify the subject within the frame, often resulting in a larger area and finer detail. While this helps the classifier focus on and learn richer class-specific features, it does differ from many real-world images where subjects can be small or part of a complex, multi-object scene. Although the content of the generated image can be constrained by designing richer text prompts, we believe that developing more adaptive algorithms to generate scenes more aligned with the original dataset is a promising direction for research.\n\n*   **Nature of \"Hard\" Samples:** For images generated to be intentionally confusing (e.g., an 'X' that fools the classifier into seeing a 'Y' via the `R_CSCA` reward), the results are particularly interesting. Visually, the generated image remains clearly identifiable as 'X' to a human observer and does not appear as a blend of 'X' and 'Y'. However, these images contain subtle, high-frequency patterns, akin to adversarial noise. As our reward signals confirm, these patterns are highly effective at misleading the classifier. We hypothesize this is because the diffusion model learns to exploit the classifier's high sensitivity to texture and fine details, which humans often overlook in favor of holistic shapes. A thorough investigation of this adversarial-like phenomenon is a compelling area for future work."}]

Author rebuttal:

Meta-review: In this paper, the authors focus on the few-shot class-incremental learning problem. They propose a method that utilizes a reward-aligned learning strategy, showing the state-of-the-art performance on few-shot class-incremental learning benchmarks. It was reviewed by four expert reviewers. All of them recommended accepting this paper. Most of the concerns are addressed during the rebuttal period. According to the reviewers, the paper is clear and well-written. The method section is very well formulated and motivated. And it also provides new insights by applying the generative-based methods for few-shot class-incremental learning. Therefore, it is a clear acceptance. The authors are encouraged to include the additional results and discussions during the rebuttal period in the final paper.
Decision: Accept (poster)