Title: Diffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL
Venue: NeurIPS 2025
Accepted: yes
Keywords: Few-Shot Class-Incremental Learning; Diffusion Model; Reward Alignment
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\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.\n\nQUESTIONS:\nPlease see the weaknesses.\n\nLIMITATIONS:\nYes, in the supplementary material.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-25"}

comments: SUMMARY:
This paper presents a reward-alignment-based method for Few-Shot Class-Incremental Learning (FSCIL).
Different 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.
To 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.
The authors show experiments for the effectiveness of the proposed methods.

STRENGTHS AND WEAKNESSES:
Strengths:
1. New insights might be provided by the generative-based methods for FSCIL.
2. The design of rewards is with strong motivation.
3. The experiment results shown are with convincing improvement.

Weaknesses:
1. 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). 
2. 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.
3. I think it lacks detailed comparisons of the proposed method and previous generative-based incremental learning methods like [1-2].

Ref:

[1]Gao, Rui, and Weiwei Liu. "Ddgr: Continual learning with deep diffusion-based generative replay." International Conference on Machine Learning. PMLR, 2023.

[2]Meng, Zichong, et al. "Diffclass: Diffusion-based class incremental learning." European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024.

QUESTIONS:
Please see the weakness above.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\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.\n\nQUESTIONS:\n1.\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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths - \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.\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}