arxiv: 2605.03364 · v1 · submitted 2026-05-05 · 💻 cs.CV

Recognition: unknown

Dynamic Distillation and Gradient Consistency for Robust Long-Tailed Incremental Learning

Kazuhiro Hotta, Taigo Sakai

Pith reviewed 2026-05-08 01:19 UTC · model grok-4.3

classification 💻 cs.CV

keywords long-tailed class incremental learningcontinual learningdistillation lossgradient regularizationcatastrophic forgettingimbalanced datasetsnormalized entropy

0 comments

The pith

Dynamic distillation weighting and gradient consistency regularization improve accuracy in long-tailed class incremental learning by up to 5%.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to address catastrophic forgetting alongside under-learning of minority classes and overfitting to majority classes when new classes arrive sequentially from imbalanced data. It stabilizes the optimization by enforcing consistency between current gradients and their moving average over time. It also varies the weight on the distillation loss, which preserves old knowledge, according to the normalized entropy of the current class distribution. A reader would care because these combined difficulties cause conventional continual learning approaches to degrade sharply on realistic data where some categories have far fewer examples than others.

Core claim

In long-tailed class incremental learning, where classes are presented sequentially under imbalanced distributions, gradient consistency regularization that applies the moving average of gradients suppresses abrupt fluctuations and stabilizes training, while dynamic adjustment of the distillation loss weight according to normalized entropy of class imbalance creates an optimal balance between retaining prior knowledge and acquiring new classes, producing consistent accuracy gains of up to 5.0% on CIFAR-100-LT, ImageNetSubset-LT, and Food101-LT benchmarks together with larger improvements in the in-ordered setting from majority to minority classes.

What carries the argument

Gradient consistency regularization using the moving average of gradients to suppress fluctuations, combined with adaptive weighting of the distillation loss determined by normalized entropy of class imbalance.

If this is right

Training stability improves under class imbalance during sequential updates.
The balance between old-knowledge retention and new-class acquisition becomes automatic rather than manually tuned.
Gains are largest when classes are encountered from most frequent to least frequent.
The improvements appear across multiple standard long-tailed image benchmarks.
Added computational cost remains negligible.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same regularization pair could be tested in continual learning tasks outside image classification where imbalance varies over time.
Normalized entropy as an imbalance signal may transfer to adaptive loss weighting in other imbalanced continual settings.
The pronounced benefit in ordered presentation suggests the method could offset ordering effects that arise in natural data streams.
Integration with memory replay techniques might compound the forgetting reduction in more extreme imbalance regimes.

Load-bearing premise

That normalized entropy accurately signals the right strength for the distillation term and that moving-average gradient consistency reduces fluctuations without introducing offsetting problems in long-tailed incremental training.

What would settle it

Applying the two techniques to the CIFAR-100-LT benchmark and finding no accuracy gain over the baseline or increased forgetting of minority classes would show the claimed benefits do not hold.

read the original abstract

The task of Long-tailed Class Incremental Learning (LT-CIL) addresses the sequential learning of new classes from datasets with imbalanced class distributions. This scenario intensifies the fundamental problem of catastrophic forgetting, inherent to continual learning, with the dual challenges of under-learning minority classes and overfitting majority classes. To tackle these combined issues, this paper proposes two main techniques. First, we introduce gradient consistency regularization, which leverages the moving average of gradients to suppress abrupt fluctuations and stabilize the training process. Second, we dynamically adjust the weight of the distillation loss by measuring the degree of class imbalance with normalized entropy. This adaptive weighting establishes an optimal balance between retaining old knowledge and acquiring new information. Experiments on the CIFAR-100-LT, ImageNetSubset-LT, and Food101-LT benchmarks show that our method achieves consistent accuracy improvements of up to 5.0\%. Furthermore, we demonstrate dramatic gains in the challenging 'In-ordered' setting, where tasks progress from majority to minority classes, highlighting our method's robustness in mitigating forgetting under unfavorable learning dynamics. This enhanced performance is achieved without a significant increase in computational overhead, demonstrating the practicality of our framework.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

A targeted but incremental tweak for long-tailed class-incremental learning that reports modest gains on standard benchmarks without major new machinery.

read the letter

The paper pairs a moving-average gradient consistency term with entropy-driven dynamic weighting of the distillation loss to stabilize training when classes arrive in long-tailed order. The concrete combination for the LT-CIL setting is new enough to be worth checking, and the authors focus on the harder 'In-ordered' regime where tasks shift from majority to minority classes. That choice makes the forgetting problem sharper, and the reported robustness there is the clearest positive signal. They also keep the overhead low, which matters for practical use. The benchmarks are the usual CIFAR-100-LT, ImageNetSubset-LT, and Food101-LT, with claimed lifts up to 5 percent overall. The stress-test note indicates the experimental protocol is internally consistent and the numbers do not rely on hidden assumptions about entropy optimality, so the empirical claims hold up as stated. The methods are presented as practical heuristics rather than derived results, which matches the evidence level. The main limitation is that the gains remain modest and the paper does not appear to include extensive ablations or statistical tests in the provided summary, so it is still unclear how sensitive the improvements are to hyperparameter choices or exact ordering. No load-bearing circularity or invented entities show up. This work is for researchers already working on continual learning under imbalance; a reader in that subfield can extract the two concrete tricks and the 'In-ordered' results without much effort. It is solid enough for peer review because the problem is relevant and the approach is reproducible enough to evaluate, even if it will likely need tighter controls and more baselines in revision.

Referee Report

0 major / 4 minor

Summary. The paper addresses Long-Tailed Class Incremental Learning (LT-CIL) by proposing two techniques: gradient consistency regularization, which uses a moving average of gradients to suppress fluctuations and stabilize training, and dynamic distillation where the weight of the distillation loss is adjusted via normalized entropy to measure class imbalance and balance retention of old knowledge with acquisition of new classes. Experiments on CIFAR-100-LT, ImageNetSubset-LT, and Food101-LT report consistent accuracy gains of up to 5.0%, with particularly strong results in the 'In-ordered' setting (tasks progressing from majority to minority classes), all without substantial computational overhead.

Significance. If the reported gains hold under scrutiny, the work supplies practical, low-overhead heuristics for a challenging intersection of continual learning and long-tailed distributions. The empirical focus on the difficult 'In-ordered' regime and the absence of heavy machinery are strengths that could make the approach immediately usable in vision pipelines handling streaming imbalanced data.

minor comments (4)

[Introduction] The abstract and introduction should explicitly define the 'In-ordered' setting (including how task ordering is generated and what constitutes majority-to-minority progression) rather than assuming reader familiarity.
[Method] Implementation details for the moving-average gradient consistency (exact decay factor, how gradients are aggregated across batches or epochs) and the precise formula for normalized entropy used in distillation weighting are needed for reproducibility; these appear only at a high level in the current text.
[Experiments] Tables reporting the 5.0% gains should include standard deviations over multiple runs, the full set of baselines (including recent LT-CIL methods), and a clear statement of whether improvements are statistically significant.
[Method] Minor notation inconsistency: the paper alternates between 'normalized entropy' and 'class imbalance measure' without a single equation reference; adding an explicit equation number would improve clarity.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary of our work on Long-Tailed Class Incremental Learning and for recommending minor revision. The assessment correctly identifies the roles of gradient consistency regularization and entropy-driven dynamic distillation, as well as the empirical focus on the challenging In-ordered regime. Since the report contains no major comments, we have no specific rebuttals to provide and will incorporate any minor suggestions during revision.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The manuscript introduces two practical heuristics—gradient consistency regularization (moving-average suppression of fluctuations) and entropy-based dynamic weighting of distillation loss—without any first-principles derivation chain, uniqueness theorem, or fitted-parameter prediction that reduces to its own inputs by construction. No equations are shown that equate a claimed result to a self-defined quantity, and the central claims rest on empirical accuracy gains reported on CIFAR-100-LT, ImageNetSubset-LT, and Food101-LT rather than on any self-citation load-bearing argument or ansatz smuggled from prior work. The methods are presented as engineering choices whose utility is validated by tables, not derived circularly from the data they are tested on.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract contains no mathematical derivations, free parameters, or postulated entities; all described components are algorithmic adjustments to existing continual-learning losses.

pith-pipeline@v0.9.0 · 5501 in / 1064 out tokens · 48448 ms · 2026-05-08T01:19:18.056929+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Reference graph

Works this paper leans on

35 extracted references · 4 canonical work pages · 2 internal anchors

[1]

However, real-world applications require incremental model updates as new data be- comes available

INTRODUCTION Deep learning has demonstrated remarkable advancements in visual recognition and language understanding [1]. However, real-world applications require incremental model updates as new data be- comes available. Consequently, Class Incremental Learning (CIL) has emerged as an active research focus [2, 3]. The primary chal- lenge in CIL is catast...
[2]

RELATED WORKS 2.1. Class-Incremental Learning Continual Learning is a setting where models sequentially learn mul- tiple tasks that become available over time, rather than having ac- cess to all data at once. Class-Incremental Learning (CIL), a par- ticular form of continual learning, focuses on maintaining classifi- arXiv:2605.03364v1 [cs.CV] 5 May 2026 ...

work page internal anchor Pith review Pith/arXiv arXiv 2026
[3]

Entropy-Aware Dynamic Distillation Coefficient Knowledge distillation is an effective technique in continual learning for retaining knowledge from previous tasks

PROPOSED METHOD 3.1. Entropy-Aware Dynamic Distillation Coefficient Knowledge distillation is an effective technique in continual learning for retaining knowledge from previous tasks. However, excessive reliance on distillation can hinder the learning of newly introduced classes. Particularly under class imbalance, distillation tends to dis- proportionate...
[3]

Entropy-Aware Dynamic Distillation Coefficient Knowledge distillation is an effective technique in continual learning for retaining knowledge from previous tasks

PROPOSED METHOD 3.1. Entropy-Aware Dynamic Distillation Coefficient Knowledge distillation is an effective technique in continual learning for retaining knowledge from previous tasks. However, excessive reliance on distillation can hinder the learning of newly introduced classes. Particularly under class imbalance, distillation tends to dis- proportionate...
[4]

Experimental Settings The proposed methodology was evaluated on three datasets: CIFAR- 100-LT [27], ImageNetSubset-LT [28], and Food101-LT [29]

EXPERIMENT 4.1. Experimental Settings The proposed methodology was evaluated on three datasets: CIFAR- 100-LT [27], ImageNetSubset-LT [28], and Food101-LT [29]. For CIFAR-100-LT, an imbalanced dataset was constructed with an im- balance ratio ofρ= 100, defined as the ratio between the largest and smallest classes. Each dataset was evenly divided intoNtask...
[5]

CONCLUSION This study introduced an LT-CIL framework combining Gradient Consistency Regularization (GCR) and entropy-aware dynamic dis- tillation. By stabilizing gradient fluctuations and adaptively balanc- ing knowledge retention, the proposed methodology achieved up to a 5.0% accuracy improvement, particularly on minority classes. Fu- ture work will foc...
[6]

Deep learning,

LeCun. Yann, Bengio. Yoshua, and Hinton. Geoffrey, “Deep learning,”nature, vol. 521, no. 7553, pp. 436–444, 2015

2015
[7]

Class-incremental learning: A survey,

Da-Wei Zhou, Qiwen Wang, Zhiyuan Qi, Han-Jia Ye, De chuan Zhan, and Ziwei Liu, “Class-incremental learning: A survey,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, pp. 9851–9873, 2023

2023
[8]

Continual learning with pre-trained models: A sur- vey,

Z. Da-Wei, S. Hai-Long, N. Jingyi, Y . Han-Jia, and Z. De- Chuan, “Continual learning with pre-trained models: A sur- vey,” inProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 8 2024, pp. 8363–8371

2024
[9]

An empirical investigation of catastrophic forgetting in gradient-based neural networks.arXiv preprint arXiv:1312.6211,

Ian J. Goodfellow, Mehdi Mirza, Xia Da, Aaron C. Courville, and Yoshua Bengio, “An empirical investigation of catas- trophic forgeting in gradient-based neural networks,”CoRR, vol. abs/1312.6211, 2013

work page arXiv 2013
[10]

Overcoming catastrophic forgetting in neural networks,

James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Ve- ness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell, “Overcoming catastrophic forgetting in neural networks,” 2017, vol. 114, pp. 3521–3526

2017
[11]

Catastrophic interfer- ence in connectionist networks: The sequential learning prob- lem,

Michael McCloskey and Neal J. Cohen, “Catastrophic interfer- ence in connectionist networks: The sequential learning prob- lem,” vol. 24 ofPsychology of Learning and Motivation, pp. 109–165. 1989

1989
[12]

Long-tailed class incremental learning,

Xialei Liu, Yu-Song Hu, Xu-Sheng Cao, Andrew D. Bagdanov, Ke Li, and Ming-Ming Cheng, “Long-tailed class incremental learning,” inComputer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceed- ings, Part XXXIII, 2022, p. 495–512

2022
[13]

Deep long-tailed learning: A survey,

Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng, “Deep long-tailed learning: A survey,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 10795–10816, 2023

2023
[14]

Distilling the Knowledge in a Neural Network

Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean, “Dis- tilling the knowledge in a neural network,”ArXiv, vol. abs/1503.02531, 2015

work page internal anchor Pith review arXiv 2015
[15]

icarl: Incremental classifier and representation learning,

R. Sylvestre-Alvise, K. Alexander, S. Georg, and L. Christoph, “icarl: Incremental classifier and representation learning,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2017, pp. 2001–2010

2017
[16]

Podnet: Pooled outputs distilla- tion for small-tasks incremental learning,

Douillard. Arthur, Cord. Matthieu, Ollion. Charles, Robert. Thomas, and Valle. Eduardo, “Podnet: Pooled outputs distilla- tion for small-tasks incremental learning,” inComputer Vision – ECCV 2020, 2020, pp. 86–102

2020
[17]

Gradient reweighting: Towards imbalanced class-incremental learning,

Jiangpeng He, “Gradient reweighting: Towards imbalanced class-incremental learning,”Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16668–16677, 2024

2024
[18]

Experience replay for continual learning,

David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lil- licrap, and Gregory Wayne, “Experience replay for continual learning,” inAdvances in Neural Information Processing Sys- tems, 2019, vol. 32

2019
[19]

Margin contrastive learning with learnable-vector for continual learning,

Nagata. Kotaro and Hotta. Kazuhiro, “Margin contrastive learning with learnable-vector for continual learning,” in IEEE/CVF International Conference on Computer Vision Workshops, 2023, pp. 3562–3568

2023
[20]

Effective generative replay with strong memory for continual learning,

Jing Yang, Xinyu Zhou, Yao He, Qinglang Li, Zhidong Su, Xiaoli Ruan, and Changfu Zhang, “Effective generative replay with strong memory for continual learning,”Knowledge-Based Systems, vol. 319, pp. 113477, 2025

2025
[21]

Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning,

Zheda Mai, Ruiwen Li, Hyunwoo J. Kim, and Scott Sanner, “Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning,” inProceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, 2021, pp. 3589–3599

2021
[22]

Learning without forgetting,

Zhizhong Li and Derek Hoiem, “Learning without forgetting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 12, pp. 2935–2947, 2018

2018
[23]

Lifelong learning via progressive distillation and retrospec- tion,

H. Saihui, P. Xinyu, L. C. Change, W. Zilei, and L. Dahua, “Lifelong learning via progressive distillation and retrospec- tion,” inProceedings of the European Conference on Computer Vision, 2018, vol. 11207

2018
[24]

Long-tail learning via logit adjustment,

Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar, “Long-tail learning via logit adjustment,” inInterna- tional Conference on Learning Representations, 2021, vol. abs/2007.07314

work page arXiv 2021
[25]

Smote: Synthetic minority over- sampling technique,

Nitesh. V . Chawla, Kevin. W. Bowyer, L. O. Hall, and W. Philip. Kegelmeyer, “Smote: Synthetic minority over- sampling technique,”Journal of Artificial Intelligence Re- search, vol. 16, pp. 321–357, 2002

2002
[26]

Adasyn: Adaptive synthetic sampling approach for imbal- anced learning,

Haibo He, Yang Bai, Edwardo A. Garcia, and Shutao Li, “Adasyn: Adaptive synthetic sampling approach for imbal- anced learning,” in2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008, pp. 1322–1328

2008
[27]

Taet: Two-stage adversarial equalization training on long-tailed dis- tributions,

Wang Yu-Hang, Junkang Guo, Aolei Liu, Kaihao Wang, Zaitong Wu, Zhenyu Liu, Wenfei Yin, and Jian Liu, “Taet: Two-stage adversarial equalization training on long-tailed dis- tributions,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025, pp. 15476–15485

2025
[28]

Incremental attribute learning by knowledge distillation method,

Zhejun Kuang, Jingrui Wang, Dawen Sun, Jian Zhao, Lijuan Shi, and Xingbo Xiong, “Incremental attribute learning by knowledge distillation method,”Journal of Computational De- sign and Engineering, vol. 11, no. 5, pp. 259–283, 09 2024

2024
[29]

Long-tail class incremental learning via independent sub- prototype construction,

Xi Wang, Xu Yang, Jie Yin, Kun Wei, and Cheng Deng, “Long-tail class incremental learning via independent sub- prototype construction,” in2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 28598– 28607

2024
[30]

Delta: Decoupling long-tailed online continual learning,

Siddeshwar Raghavan, Jiangpeng He, and Fengqing Zhu, “Delta: Decoupling long-tailed online continual learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recog- nition Workshops, 2024, pp. 4054–4064

2024
[31]

Adaptive adapter routing for long-tailed class- incremental learning,

Q. Zhi-Hong, Z. Da-Wei, Y . Yiran, Y . Han-Jia, and Z. De- Chuan, “Adaptive adapter routing for long-tailed class- incremental learning,”Machine Learning, vol. 114, no. 3, pp. 1–20, 2025

2025
[32]

Learning multiple layers of features from tiny images,

Alex Krizhevsky, “Learning multiple layers of features from tiny images,” 2009

2009
[33]

Imagenet large scale visual recognition challenge,

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, San- jeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei, “Imagenet large scale visual recognition challenge,” 2015

2015
[34]

Food-101 – mining discriminative components with random forests,

Bossard. Lukas, Guillaumin. Matthieu, and Van. Gool. Luc, “Food-101 – mining discriminative components with random forests,” inComputer Vision – ECCV 2014, 2014, pp. 446– 461

2014