Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment
Authors: Alif Ashrafee
Jędrzej Kozal
Michał Woźniak
Bartosz Krawczyk
Venue: Accepted by TMLR ()

Abstract: Traditional continual learning methods prioritize knowledge retention and focus primarily on mitigating catastrophic forgetting, implicitly assuming that the data distribution of previously learned tasks remains static. This overlooks the dynamic nature of real-world data streams, where concept drift permanently alters previously seen data and demands both stability and rapid adaptation. We introduce a holistic framework for continual learning under concept drift that simulates realistic scenarios by evolving task distributions. As a baseline, we consider Full Relearning (FR), in which the model is retrained from scratch on newly labeled samples from the drifted distribution. While effective, this approach incurs substantial annotation and computational overhead. To address these limitations, we propose Adaptive Memory Realignment (AMR), a lightweight alternative that equips rehearsal-based learners with a drift-aware adaptation mechanism. AMR selectively removes outdated samples of drifted classes from the replay buffer and repopulates it with a small number of up-to-date instances, effectively realigning memory with the new distribution. This targeted resampling matches the performance of FR while reducing the need for labeled data and computation by orders of magnitude. To enable reproducible evaluation, we introduce four concept drift variants of standard vision benchmarks: Fashion-MNIST-CD, CIFAR10-CD, CIFAR100-CD, and Tiny-ImageNet-CD, where previously seen classes reappear with shifted representations. Comprehensive experiments on these datasets using several rehearsal-based baselines show that AMR consistently counters concept drift, maintaining high accuracy with minimal overhead. These results position AMR as a scalable solution that reconciles stability and plasticity in non-stationary continual learning environments. Full implementation of our framework and concept drift benchmark datasets are available at: https://github.com/AlifAshrafee/CL-Under-Concept-Drift.

Published in Transactions on Machine Learning Research (01/2026)
Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment
Alif Ashrafee
aa5264 @ritt edu
Rochester Institute of Technology, New York, USA
Jędrzej Kozal
Bedraej.kzzal@yurr.cde
Wroclaw Universiby of Science and Technology, Wroclaw, Poland
Michat Wožniak
Brrchal.wwommiika@apur edu.pl
Wroclaw University of Science and Technology, Wroclaw, Poland
Bartosz Krawezyk
bertoss.kkraucryy@iiitccc edu
Rochester Institute of Technology, New York, USA
Reviewed on OpenReview: https: openreview. net/ forum? id- IdrDL
Abstract
Traditional continual learning methods prioritize knowledge retention and focus primarily on mitigating catastrophic forgetting, implicitly assuming that the data distribution of pre- viously learned lasks remains static. This overlooks the dynamic nature of real-world data streams, where concept drift permanently alters previously seen data and demands both stability and rapid adaptation. We introduce a holistic framework for continual learning under concept drift that simulates realistic scenarios by evolving task distributions. As baseline, consider Full Relearning (FR), in which the model is retrained from scratch newly labeled samples from the drifted distribution. While effective, this approach in- curs substantial annotation and computational overhcad. To address these limitations, we propose Adaptive Memory Realignment (AMR), a lightweight. alternative that equips rehearsal- based learners with drift-aware adaptation mechanism. AMR selectively re- moves outdatod samples of drifted classes from the replay buffer and repopulates it with a small number of up-to-date instances, effectively realigning memory with the new distribu- tion. This targeted resampling matches the performance of FR while reducing the need for labeled data and computation by orders of magnitude. To enable reproducible evaluation, we introduce four concept drift variants of standard vision benchmarks: Fashion-MNISTI CD, CIFARIO-CD, CIFAR100-CD, and Liny-ImageNNe-CCD where previously seen classes reappear with shifted reprosentations. Comprehensive experiments on these datasets using several rehearsal- based baselines show that AMR consistently counters concept drift, main- taining high accuracy with minimal overhead. These results position AMR as scalable solution that reconciles stability and plasticity in non-stationary continual learning environ- ments. Full implementation of framework and concept drift benchmark datasets available at: https: https://githin
1
Introduction
In recent years, Uhere has been a lot of progress (Masana et al. 2020
Kirkpalrick et al. 2016 Chaudhry
et al. 2018 Buzzega et al.) 2020aa Arani et al.) 2022 Zhuo et al. 2023) in Continual Learning (Chen & Liu 2018 rescarch. The key challenge this field to ensure that models can learn over time while retaining information from earlier tasks and mitigating catastrophic forgetting (French] 1999) - phenomenon where previously learned knowledge is overwritten by new information. Despite this progress, much of the existing

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work assumes that the properties of past data remain static (Masana et al. 2020 once learned. While
simplifying the problem, such an assumption overlooks the dynamic nature of real-world data streams (Gama et al. 2014a), where concept drift- -sifts in the statistical properties of previously seen data is frequent occureence.
Concept
Evolved Concept
Visual Drift
Initial Distribution
Incremental Task
Concept Drift
Figure 1: Visualization of concept drift in continual learning. (a) Initial Distribution: The learning process begins with class of kittens. (b) Incremental Task: A new task introduces adult dogs, prompting the model to form decision boundary that separates kittens from dogs. (c) Concept Drift: Over time, kittens evolve into adult cats, and adult dogs are replaced by puppies. Although the class labels remain the same (cats vs. dogs), their visual representation shifts, requiring an update in the decision boundary to maintain correct classification.
Concept drift (Widmer& & Kubat 1993) poses a unique challenge for continual learning, as requires models not only to retain knowledge but also adapt changes in previously encountered classes (see Figure I In traditional continual learning, the changes in distributions of already learned classes considered in the Domain- Incremental scenario (van de Ven & Tolias 2019), where new classes introduced over time, On the other hand, the Mlass-nncrement.. scenario (van de Ven & Tolias 2019 Korycki & Krawczyk 2024] assurnes that changes in data distribution occur only by intruingg completely new classes, with no shifts in previously learned ones. However, the combination of both scenarios (Korycki & Krawczyk, 2021: Lyu et al. 2024) is rarely addressed in the literature. Recently introduced Class- Laass-Incrementll Learning with repetition scenario (Hemati al.] 2023) assumess that past classes can reappear in the future, but with distribution of class remaining the same. Considering changes in both past and current class distributions could lead to development of more ficxible algorithms, that could handle complexity more casily.
Among the various strategies developed for continual learning, rehearsal methods (Buzzega et al. 2020a; Caccia et al.] 2022 Zhuo et al., 2023 Arani et al. 2022) have achieved remarkable success. These methods maintain a small memory buffer of past data samples (Chaudhry et al. 2019). which are replayed during training to mitigale rophit forgetting. Korycki & Krawezyk (2021) adapted rehearsal algorithms for continual learning under concept drift by using class centroids to determine whether past representations should be relabeled as new class. While their approach improves performance, it has several limitations. First, it overlooks the widely adopted reservoir sampling algorithms (Vitter 1985), which are now standard in most rehearsal-based methods. Second, the assumption that past representations can be reused as new classes conflicts with certain types of concept drift. In cases of domain shift, where class feature distributions change, newly sampled examples may have entirely different representations. Relabeling old samples after drift detection may not enhance classification accuracy and could unnecessarily occupy buffer memory.
Data Streams and Continual Learning two sides of the same coin: Concept drift and evolving data distributions expose the complementary strengths and limitations of continual learning and data-stream mining. Although these two fields have historically developed along parallel trajectories, they address funda- mentally intertwined aspects of learning in dynamic environments. Whereas continual learning stresses knowledge relention, safeguarding past information against, catastrophic forgetting, data-stream mining stresses knowledge adaptation, enabling models to respond quickly and accurately to evolving data dis- tributions and concept drift. A method that focuses solcly on adaptation may achieve only locally optimal performance by discarding valuable long-term knowledge, whereas one that clings rigidly prior knowledge risks retaining outdated or irrelevant information. Unifying these perspectives is therefore essential for de-
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veloping learning systems that are both resilient and adaptive. By bridging insights from both fields, we aim to advance toward holistic approaches that can simultaneously remember and evolve.
Main contributions: To the best of our knowledge, we present the first contimuallearning framework that explicitly accounts for representation-leel concept drift. Our solution couples a lightweight drift-detection module with Adaptive Memory Realignment. (AMR): a drift-aware bufferupddate strategy that preserves relevant past knowledge while rapidly adapting to evolving distributions. By addressing representation shift directly, the framework models real-world non-stationary streams more realistically and integrates scamlessly with existing continual-learning architectures. Our contributions are:
A framework for continual learning under concept drift. We design framework for continual learning that enables the simulation of diverse concept drift scenarios across multiple benchmark datasets and severity levels through set of configurable parameters.
Concept-drif---daptiie memory. We propose AMR, buffer-update mechanism that mitigates gradient misalignment by selectively removing outdated samples while maintaining robustness to catastrophic forgetting.
Efficiency in data and compute. Extensive experiments Fashion-MNIST, CIFAR10, CI- FAR100, and Timy-ImageNet show that AMR recovers accuracy after drift with minimal labeled data and low computational overhead.
Together, these contributions provide scalable, holistic solution that reconciles stability and plasticity for continual learning in truly non-stationary environments.
2
Related Work
Continual Learning: Efforts to address catastrophic forgetting can be broadly categorized into three approaches MMsana et. al. 2020): rehearsal methods, regularization techniques, and network expansion
strategies.
Regularization methods constrain the learning process to reduce forgetting. Kirkpatrick et al. (2016) pro- posed a regularization approach using the Fisher Matrix to preserve key parameters from previous tasks, Similarly, Zenke et al. (2017) introduced a per-parameter regularization technique based on quadratic loss. Another method, presented in Li & Hoiem (2016), use predictions from a model trained prior tasks as knowledge distillation- based regularizer for new tasks. Petit et al. (2023) developed pseudo-feature generation strategy that freezes the backbone after the initial task. In the work of Zhuang et al. (2024), a frozen backbone is also utilized, but the authors frame Continual Learning as a Concatenated Recursive Least. Squares problem to compule weight updates through a closed-form solution.
Rehearsal methods mitigate forgetting by retrieving data from past tasks using memory buffers. Even introducing a few learning examples from past tasks (Chaudhry al.] 2019) could limit forgetting. More advanced rehearsal methods incorporate knowledge distillation (Buzzega el. al. 2020a). asymmetrical cross-
entropy loss (Caccia et al. 2022) or gradient projection (Chaudhry et al. 2018h. Some methods use data
from the buffer to reduce the recency bias in the classifier layer (Wu et al. 2019). Other methods specify
what samples should be selected for storage (Buzzega et al. 2020b Aljundi et al. 2019 or how to effectively retrieve samples from the buffer (Harun et al. 2024). Recently, weight interpolation between old and new networks was proposed as a complementary mechanism experience replay [ozal al. (2024).
bechitecture-based algorithms (Rusu et al 2016) address catastrophic forgetting by expanding network capacity. Typically, they mitigate forgetting by freezing parameters associated with previous tasks (Rusu) et al. 2016). However, adapting to concept drift can be challenging with such approaches, and as a result, this category will not be the focus of our work.
Adaptive and plastic Continual Learning: Recent continual learning research incrcasingly targets adap- tive plasticity as first-class objective beyond mitigating forgetting, by explicitly decoupling stable and plas- tie components in the learning dynamics and parameterization. For instance, Liang & Li (0223 proposes loss
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decoupling to separate objectives that govern new-versus-old discrimination and new class learning to better control the stability-plastiicl icity trade-off in task-agnostic CL. Prompt-based designs similarly split functional- ity into modules specialized for retention and rapid acquisition. PromptFusion (Chen et al. 2024) introduces dedicated stabilizer/ booster prompts to disentangle forgetting mitigation from learning new tasks. In the lonndation-model regime, SD-LoRA (Wu el al. 2025) improves scalability by decoupling the magnitude and direction of low-rank updates, enabling strong stability-plastiiit behavior without rehcarsal. Complement- ing architectural/ /aarametrr decoupling, Self-Normalized Resets (Farias & Jozefiak] 2025) directly combats plasticity loss by selectively resetting neurons deemed inactive, restoring the model's s ability to adapt late in long task streams. Finally, principled combination strategies such as BECAME (Li et al. 2025) use Bayesian formulations to derive task-adaptive model-merging coefficients, aiming to preserve prior knowledge while maintaining learnability of new lasks, alongside theory-driven continual meta-learning approaches such as Chen et al. 2023 that dynamically adjust update behavior under shifting environments.
Concept drift: Concept drift has been widely studied (Gama ct al. 2014a] in the context of streaming data and evolving environments (Duda et al. 2001). Most works have focused on detecting and adapting to shifts in data distributions (Lu et al. 2018), distinguishing between sudden, incremental, and recurring drifts, Two major approaches include (i) using drift detectors (Van Looveren et al. 2024) to signal when model should be updated to align with the new data distribution and (fi) employing online learners with forgetting mechanisms (Bifet et al.) 2009) to implicitly adapt to the current state of the streaming environment. Moreover, addressing concept drift requires an understanding of its underlying causes (Krawezyk et al, 2017), whether due to changes in feature distributions (covariate shift), class boundaries (real drift), or latent dynamics in data streams. While most works in the concept drift domain have focused on shallow learning models, the deep learning community has recently started to show increasing interest (Xiang et al. 2023 in this research area.
Recent advances in concept drift and data stream mining have focused on both detection methods that scale to high-velocity streaming data and adaptation mechanisms that enable robust model updating under non-stationarity. One notable direction is unsupervised and representation--ueell drift detection, exempli- fied by the Maximun Concept Discrepancy Drift Detector (MCD-DD) (Wan et al.] 2024), which leverages contrastive emboddings to detect drift without reliance on labels, outperforming classical statistical tests in high-dimensionall streams. Complementary to this, Greco al. (2025) introduced DriftLens, an unsuper- vised framework designed for real-time characterization of concept drift from deep feature representations, addressing limitations of existing methods in accuracy and real-time execution. Neighbor-Searching Dis- crepancy Drift Detection Scheme (Gu et al.) 2024) isolates real concept drift by measuring classification boundary shifts bet ween samples, a key step toward minimizing false alarms from virtual drift. On the deep learning front, AF-DD (Hu et al. 2025a) is a hybrid autoencoder and deep neural network approach that combines representation learning with reconstruction-best based drift signals, demonstrating superior de- tection accuracy on synthetic and real-world streams compared to shallow methods. Transfer learning and multi-source strategies have also emerged, such as MARLINE (Du et al. 2025), which uses multi-source mapping to transfer knowledge in non-stationary environments and improve data stream prediction under drift. Lite-RVFL. (Hu et al., 2025b) is a random vector functional-link network that adapts to concept drift without explicit detection or retraining, emphasizing recent data through exponential weighting. Harshit & Mounvik (2025) also integrated transformers with autocncoder reconstruction and multiple drift metrics to enhance early detection sensitivity and robustness.
Concept drift in Continual Learning: First discussion of concept drift in Continual Learning scenarios was carried out in Cossu et al. (221)) where authors suggested that excessive focus on Class-Incrementt learning is too restrictive, as past classes could repeat over time in natural environments. In Korycki & Krawezyk (2021), ), an experience replay-based method with enhanced memory management for concept drift was introduced. It used class centroids to determine whether past samples should be relabeled. However, this approach oversimplifies concept drift by reducing the problem to two meta-labels (like vs. dislike), failing to capture the complexities of concept drift adaptation in multi-class large-scale datasets. Moreover, the authors define concept drift solely occurring through changes in data representations. This assumption does not align with many real-world continual learning scenarios where labels remain unchanged while data representations evolve. In Casado
shift in class labels over time, overlooking the possibility of drift
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et al. (2021] , novel method for federated learning was introduced that considers the possibility of concept drift, but does not explicitly measure robustness to its occurrence. In GGooez Villa et al. (2024). the authors raise concerns about semantic drift, which causes the prototypes of learned classes to shift in feature space as new classes are introduced. Although their proposed learnable drift compensation mitigates this shift, it does not address the possibility of recurring classes or how to compensate for drift previously seen classes reappear with altered representations.
Concept drift test-time adaptation: attention from the Continual Learning community (Hong et al. 2023 Ni et al. 2025). It is important to highlight that While both concept drift and TTA address distributional shifts, they represent fundamentally distinct challenges in Continual Learning. Concept drift refers to the gradual or abrupt change in the underlying data distribution over time, necessitating continual model updates to remove outdated knowledge and update the stored past task information. In contrast, test-time adaptation focuses on rapid, often unsupervised adjustments to distribution shifts encountered during inference (Zhu et al. (024).. Crucially, concept drift emphasizes long-term updates of the past knowledge stored in the model, whereas Lest-Lime adaptation operates in single-task setting and prioritizes immediate robustness.
Recently, test-time adaptation (TTA) has attracted increasing
Critical gap in Continual Learning Literature: Existing continual learning algorithms suffer from a vital limitation: they either ignore the recurrence of classes with altered representations or reduce concept drift to label-level changes, failing to capture the evolving shifts common in real-world data streams. In dynamic environments such as ecological monitoring or autonomous driving, previously seen classes can reappear under varying noise, lighting, or weather conditions, leading to representation-let drift. Our work addresses this underexplored problem by explicitly modeling concept drift changes in image representations of recurring classes.
3 Proposed Framework
3.1 Toward Holistic Continual-Leariigg Paradigm
In continual learning settings, it is often assumed that once a class or task has been learned, it remains stationary over time. However, real-world environments often violate this assumption, as previously acquired knowledge may bocome outdatod as concept drift alters the underlying distribution. Even tasks that appear stationary may shift over time because of lighting changes, seasonal effects, sensor noise, other unforeseen factors. Consequently,
maninual--eerningg system must not only accommodate new classes or tasks but also detect and adapt to distributional changes in classes it has already encountered. If left unaddressed, such drift renders stored representations invalid or misleading. Contemporary continual-leariigg methods, which focus primarily on retention, struggle in these situations and fail to adjust their predictions or internal representations to match new realities. A truly holistic and adaptive continual-learning framework must therefore revisit and revise prior knowledge whenever drift is detected, preserving relevance and accuracy for both old and new information.
3.2 Problem Formulation
In the context of continual learning, we formalize our problem as a sequence of tasks T = T2.... Tnb arriving over time. Fach task T₃ is associated with set of classes C, (c/, ch...., C,,) where my denotes the number of classes task T,. The goal is to train model fo X - y, parameterized by 0, where the data distribution D evolves over time. The model is trained incrementally on task-specific datasets D. {(z, y) iz E X,y V.). where YA C y corresponds to the labels associated with C. After observing task T, the model is expected to perform well on all previously seen tasks {Ti, T,...,, T.}.
Unlike standard class-incremental learning (CIL), where previously seen class distributions are assumed static, our setting accounts for evolving class semantics due to non-stationary environments. In other words, we extend the standard CIL setting where previously encountered classes may reappear with shifted distri- butions, a phenomenon known as concept drift (Gama et al. 2014b Widmer & Kubat] 1996). Specifically, for any class E C2 from a previous task T2 (j i), let Dy(c) denote the distribution of class in task T₁
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and (c(c) the corresponding distribution in task Tç. Concept drift occurs when the class reappears in later task with a new domain representation (Shin et al. 2017). indicating a distribution shift:
Di(c) + Ds(c, for some e Cj nC;,
Given the demonstrated effectiveness of rehearsal- based continual learning methods, proposed framework leverages memory-bane based strategies to address concept drift. Rehearsal methods maintain a bounded episodic memory buffer M of fixed capacity [M]. For each class let Me c M denote the subset of memory allocated to class c. The training objective at task T, is formulated as an empirical risk over the current task data and the memory bulfer:
(()) E(zrg)) [e(fo(z), y)] ((r.))m((((ffoo)) v)l.
where & denotes the loss function ccross-entropy) D Deureett = Dr is the data for the current task.
3.3 Concept Drift Detection
Our framework assumes a dynamic best-then-tane paradigm, similar to that proposed in Bifet et al. (2010) and Sun et al. (2020), which ensures that adaptation occurs reactively in response to observable changes the environment. Specifically, we monitor the lest-time distribution of previously seen classes as they reappear. This distribution compared against reference distribution, which captures the historical statistics of class Only if distributional shift is detected at test time do proceed to adapt the model to the updated distribution D,(c). This setting mirrors a data stream scenario with dynamic and monitored test stream AAgrahari & Singh 2022 enabling early detection and timely response to distributional shifts.
We incorporate an uncertainty-beet based drift detection mechanism using the two-sample Kolmogorov Smirnov (KS) (Massey 1951) test. Let fo for input z E X, where K | is the total number of classes. The uncertainty associated with each input is quantified using predictive entropy computed from the softmax of the logits. Specifically, we define the uncertainty function as:
R k denote fixed pre-trained backbone that outputs class logits
K = Σ Pa(()) log Pog))
u(x) = ((sottaxx(f(()))
km1
where (1) is the softmax probability for class k. To detect concept drift for recurring classes, compare uncertainty distributions from two sources:
The reference distribution Uref, computed using uncertainty values from class-specific samples stored in the memory buffer M, from previous tasks.
The test distribution Miest, computed from uncertainty values of incoming samples for the same class in the current test task.
We compute the Saanogorov-Smirnon statistic:
DRs sup FFeea Pitcos (u)l.
where Fu(u)) denotes the empirical cumulative distribution function (ECDF) of uncertainty values. A concept drift event is flagged when:
DKS >
where 8 is a fixed threshold that governs the sensitivity of the drift detector.
3.4 Adaptive Memory Realignment (AMR) for Drift Adaptation
Once concept drift is detected for a class c, our adaptation mechanism, Adaptive Memory Realignment (AMR), updates the memory buffer to reflect the new distribution. Let the memory buffer be denoted as M {(0 9;) MMM and define the index set for class
I- = (1,.... |MI] yj = c]

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The adaptation process consists of the following steps:
Detect Drift: Based on the KS statistic, identify the set of drifted classes Carift S C2 for the current
task T,
Flush: For each class e E Carift, remove outdated samples of class c from the buffer by setting M[j] = 0 for all Ic.
Resample: Repopulate the freed memory slots with updated instances drawn from the new distri- bution D.(c). For each j E Ie: sample a new instance x ~ Dj(c), and set MMj] = (a))r. c).
Algorithm Concept-Drift. Adaptive Memory Realignment
Input: Task stream T (Ti ................................ TN}, model fo, memory buffer M, drift significance threshold 6 Output: Updated model fo
3: Initialize: 0 < random init, M + 0, Npast +-
4: for T do
Receive data = = ((x,)) y V/} for C Vi CEVi N do
Uhot {2((x) (x, M, y = cj
Uhest < [1(xx) (x, v) D,, c}
DKS x- sup, (u) Fu (1)
10:
if DKs > 8 then
> Drift detected
11:
I. + E I....,,MMI... = =c for I do Sample zhow Di(c) Mii] (zgov,
12:
13:
14:
15:
end for end if
16:
17:
end for
18:
Train So on D,UM with loss:
[(0) ED. ((fooz)()) Em(((f(()))
19:
M + ReservoirSSamplingg D;)
20: Ypast t- Ppsst. U\:
21: end for
22:
return
This targeted realignment ensures that the memory buffer reflects the most recent distribution Dz(c) for each drifted class c Carift, mitigating negative transfer from outdated samples and promoting alignment with the most recent evolving distribution. Figure 2 illustrates the working principles of the proposed algorithm.
3.5
Theoretical Analysis
3.5.1
Gradient Misalignment Analysis
We now analyze why maintaining outdated representations in the memory buller impedes learning. Consider the gradient of the loss function C(O) during rehearsal training:
VoL(0) (( (V((fo(z) Voll)
Curvent task gradient
Rehearsal gradient
When concept drift occurs, the memory buffer contains samples from the old distribution (c(c) for a drifted class e, while current data follows D.(c).

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Memory Buffer from Task k-1
Recurinn
CCO
Drift Detector
Task k
Frozen
Trainable
New Class
CIL Model
Memory Buffer from Task
Reservoir Sampling
Ref, Samples
Removal of Outdated Samples
Drifted
Drift Detector
Task
k 1
Memory Realignment
New Class
CIL Model
Reservoir Sampling
Figure Flow of our proposed Concept-Driff Adaptive Memory Realignment method for continual learning under concept drift. The approach integrates uncertainty-based drift detection module with adaptive memory manage- ment to selectively retain and update buffer samples in the presence of recurring classes exhibiting distributional shifts.
For drifted class a with sufficiently different distributions (c) and P:(c), the expected gradients from these distributions exhibit interference, leading to misaligned parameter updates.
Theorem 1:
Proof: Let Gold = (( (VL((((())))))) v)] and Gnew (e) (o) v) denote the expected gradients from the old and new distributions, respectively. The cosine similarity between these gradients quantifics thoir alignment:
sim (oold Gnew) = Gold Gnew
||Gotall Il [|Gnewll
Under significant drift, this similarity decreases and can become negative. The effective gradient during training becomes:
Geflective = Gnew + (1 - a)) Gold a Gnew
where a represents the fraction of updated samples of class e in the memory bufler. When a (no buller update), Goffective Gnew Gold, which can deviate significantly from the optimal direction Gnew when sim(Gotd, Gnew) is low. To quantify this deviation, define the gradient alignment efficiency:
Geflective Gnew
Malign
Geffeetivell [[
For non-drifted classes, Mllggn 1, indicating efficient learning. For drifted classes with outdated represen- tations in the buffer, Maligu < and potentially Maliga < under severe sudden drift, as occurs in our problem setting, resulting in inefficient learning.
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To tackle this gradient misalignment, need better sampling strategy than reservoir sampling. This is because the probability of reservoir sampling effectively replacing the outdated samples from the memory buffer is negligibly small, as we will see in the next section.
3.5.2 Limitations of Conventional Reservoir Sampling
We now remonstrate why conventional reservoir sampling is suboptimal for handling concept drift compared to our targeted replacement strategy.
With standard reservoir sampling, the probability of effectively replacing all outdated samples of drifted classes reaches zero as the number of classes increases.
Theorem 2:
Proof: For a memory buffer of size [M containing K classes with approximately equal representation, each class occupies approximately [Mel 2 K probability that a specific old sample in the buffer is replaced under reservoir sampling is:
[M] memory slots. For drifted class e with new samples, the
P(replaced)
II
1hc
[M)
211 exp (-M²)
The
For ne < MM, which is typical in continual learning, this approximates to: P(replaced) =
T
[M]
The expected number of replaced samples from class e is:
IM
ne
ne
Elreplaced samples from c| = |Mel P(replaced)
K
M
K
This implies that with ne new samples and K classes, standard reservoir sampling only replaces approximately [MI samples typically allocated to each class. The probability of The outdated samples- far fewer than the K
K replacing all outdated samples of class is:
(MM (M))
P'replace all)
1m.2/Ma1
For practical values of [M], K, and ney this probability becomes vanishingly small as the decay is combina- torial. D
3.5.3 Optimality of Targeted Memory Realignment In this section, we discuss why targeted buffer resampling provides the optimal gradient alignment when sudden drift occurs.
Theorem 3: Targeted replacement of memory samples for drifted classes (a = 1) maximizes gradient alignment efficiency, achieving performance comparable to training on the entire sample size of the drifted distribution.
Proof: With complete targeted replacement, the effective gradient becomes:
Goflective = Gnew + 0 Gold + 1 Gnew = 2 Guew
This preserves the optimal gradient update direction while only scaling the magnitude, resulting in Valign 1. Thus, our adaptation strategy ensures that. gradient updates follow the same trajectory as they would if training from scratch on the new distribution.
This also ensures retention knowledge nri-drifted classes through the memory buffer the non con-drifted concepts remain intact in the memory without the risk of potentially being replaced by random sampling.

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3.5.4 Gradient Alignment with AMR
This section provides further justification on how the gradient alignment efficiency increases with AMR.
Theorem 4: The gradient alignment efficiency Valign monotonically increases with the proportion a of updated samples in the memory buffer, with optimal alignment achieved at a
Proof: Recall that:
Geffective Gnew + (1 a) Gold Gnew (1+ a) Gnew + (1 a) Gold
The alignment efficiency is:
Geffective Gnew
Malign
Geffectivel
|[
Substituting and simplifying:
(1 + G 2 + (1 - Gold Gnow |[newll
Valign
Geffectivel!
Taking the derivative with respect to Under the condition that Gold, Gnew) < 1, which holds under significant sudden drift, we have:
dmalig
Gnew 1|? Gold Gnew d (IGettectivel] -1 ||Gnell da |Gnew]]
da
Geffectiiee
|[Gnow 1 Gold Gnew > 0
and
d [[Geffecctvvel da |G
>> 0
Therefore,
dralign > 0, proving that the alignment efficiency increases monotonically with reaching its da
maximum at a = (complete replacement).
3.5.5 Conclusion
Our mathematical analysis demonstrates that the proposed memory adaptation strategy effectively addresses concept drift in dass-incrrmental learning by:
Eliminating misaligned gradient interference from outdated representations
Overcoming the limitations of conventional reservoir sampling
Maximizing gradient alignment efficiency through targeted buffer updates
We validate our claims through targeted experiments in Section 5.1
4 Experimental Setup
We use standard benchmarks from the continual learning literature and adapt them to incorporate concept drift:
Datasets:
Split Fashion-MNIST (Xiao al. 2017): Comprises 70,000 grayscale images size 28x28 (60,000 for training and 10,000 for testing) across 10 classes. The dataset is split into 5 tasks, each containing
2 classes.
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Split CIFAR10 and Split CIFAR100 Krizhevsky 2012
Both datasets consist of 50,000 train- ing and 10,000 test images of size 32x32. CIFAR1O is divided into 5 tasks with 2 classes per task, while CIFARIO0 is divided into 10 tasks with 10 classes per task.
Split Tmag-emageNet. [Le & Vang 2015 A subset of ImageNet (Russakovsky et al. 2015 con- taining 100,000 training and 10,000 test images of size 64x64 across 200 classes. It is split into 10 lasks, each with 20 classes.
To induce concept drift, we apply various image transformations (Hendrycks Dictterich, 2019) at several severity levels. As detailed in Appendix A the highest-severity permutation provides the most pronounced distribution shift and is therefore used in all experiments.
In standard inasemnncrllltal (CIL) benchmarks, tasks contain disjoint class sets. Our framework reintro- duces previously seen classes together with new ones when drift occurs. We denote these drift-angmented variants with the suffix "-CD" (for Concept Drift). To ensure a broad evaluation, we test both single and multi-drift scenarios over short and long task streams:
Short streams (5 tasks): S-FMNIST-CD and S-CIFARIO-CD, each with 1 and 2 drift occurrences.
Long streams (10 tasks): S-CIFAR100-CD and S-Tiny-ImageNee-CCD, each with 2 and 4 drift occurrences.
Additionally, we evaluate AMR on the CLEAR-10 dataset (Lin et al., 2021 to assess performance under natural temporal drift. These real-world drift experiments are detailed in Appendix
Experience replay methods: We base our experimental evaluation around the popular rehearsal methods:
Experience Replay (ER) CCauddryy et al. 2019 : Vanilla experience replay that revisits a subset of
past samples to consolidate past knowledge while learning from new data,
Experience Replay with Asymmetric Cross Entropy (ER-ACE) (Caccia et al. 2022 Experience
replay with asymmetrical loss to reduce representation overlap of new and old classes,
Dark Experience Replay+++ (DER++) [Buzzega et al. 2020a : Experience replay with knowledge distillation from past tasks,
Strong Experience Replay (SER) (Zhuo et al. 2023 Experience replay utilizing prediction con-
sistency between new model mimicking future experience on current training data and old model dist,iling past. knowledge from the memory buller,
Complementary Learning System-based Experience Replay (CLS-ER) (Arani et al., 2022 Experi- ence replay with dual memories: short-term and long-term that acquire new knowledge by aligning decision boundaries with semantic memories.
Hyperparameter and Implementation Details: Our incremental learning framework with concept drift was implemented on top of the Mammoth library (Buzzega et al.) 2020a). All experiments use a ResNet-18 backbone trained from scratch (no pre-training) with the Stochastic Gradient Descent (SGD) optimizer. We conduct additional experiments with ResNet-152 and ViT-S backbones in Appendix C to verify that AMR achieves similar drift recovery with larger and more modern architectures. The results confirm that AMR's effectiveness is architectureinndeeender justifying our use of ResNet-18 for computational efficiency throughout the main experiments. Rehearsal methods utilize reservoir sampling (Vitter) 1985) for buffer management. Algorithm and dataset-specific hyperparameters are adopted from the optimal values reported by the original papers wherever possible and are detailed in Appendix D
We omit standard augmentations during training and rehcarsal, as they modify image representations and adversely affect the drift detoctor's performance. The drift detector relies on original image representations stable reference to identify image representation changes over time. For drift detection, we employ
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Van Looveren et al. (2024 's uncertainty-based detector, which uses pre-trained ResNet-18 model with ImageNet1k weights. Statistical tests for drift detection are conducted at a significance level of 0.05.
Metrics: We evaluate all the methods using the following two standard metrics used in the literature:
Aberae (FAA): The Final Average Accuracy measures the model's overall perfor- mance on all seen tasks after training on the entire task stream. Let A.,j represent the accuracy on task j after training on task i. For a task stream with N tasks, the FAA is computed as:
N FAA
ANd.
N
==1
where ANJ is the accuracy task j after training the final task N. Higher values of FAA indicate better overall retention and performance across tasks.
(orgettinggF): Forgetting quantifics the loss in performance on task due learning subscquent lasks. For task j, the lorgetting score is the difference between the accuracy immedialely after learning task and its accuracy after the entire task stream:
max Ak.j AN.j k2j
where max;2j A is the highest accuracy on task j during training and ANg is the final accuracy on task j. The overall forgetting metric the average forgetting across all tasks:
N-1 F=
Σ
P
N -
j=1
Lower forgetting scores indicate better retention of previously learned tasks.
5 Experiments
5.1
Empirical Validation of Theoretical Claims
To validate the theoretical claims made in section 3.5 we conduct empirical experiments comparing three adaptation strategics under concept drift:
Vanilla: Baseline for particular rehearsal method without any drift adaptation mechanism.
AMR (Adoptive Memory Realignment): Our proposed approach that selectively replaces outdated samples in the memory buffer with new instances of drifted classes, without requiring additional data for retraining.
Full Relearning (FR): A drift response that retrains the model using a full set labeled samples from the drifted class distribution.
For each strategy, we evaluate:
Number of labeled samples required for drift adaptation,
Computational resource consumption in relative runtime and GFLOPs,
Final average class-incremental accuracy after adaptation to concept drift.
These experiments are conducted on CIFAR10-CD and CIFAR100-CD datasets under different memory buffer capacities (IM) 500 and 5000).
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We first verify Theorem 1, which predicts gradient misalignment when a previously learned class undergoes concept drift. Our hypothesis states that if the distribution of a learned class shifts in later task, the gradients computed from its new features will diverge from those based on the old features stored in the replay buffer. To illustrate this shift, we visualize the feature distributions the two classes from task 1 of CIFARIO as training progresses through five tasks. Because past. training data are unavailable during class-incremental learning, we plot the test samples of task after each subsequent task learned. We use Uniform Manifold Approximation and Projection (UMAP) (MeInnes & Healy) 2018) to project the high- dimensional features onto 2D space for visualization. UMAP is a non-linear dimensionality reduction technique that preserves the local and global structure of the feature manifold, making it well-snited for tracking evolving feature distributions across tasks. Figure Ba shows that in the absence of drift, the two classes remain linearly separable. In contrast, Figure 3b depicts the scenario in which concept drift occurs at task 3. In this case, the model can no longer produce linearly separable features for the two classes using stale buffer, leading to overlapping representations. Without adaptation, such overlap yields sub-optimal gradient updates and drop in accuracy on the drifted classes, which supports our claim in Theorem 1.
Figure highlights the computational efficiency trends of AMR in terms of both relative sample requirement 4aa runtime (4b). and FLOPs (4c). While FR requires
passes on large mumber of labeled examples, AMR limits adaptation to small, targeted memory realignments. This validates Theorem 3, which showed that full replacement, drifted samples restores optimal gradient alignment without the cost of full- scale retraining.
As shown in Figure 5 AMR closely matches the accuracy improvements of FR for both single and multiple drift occurrences but achieves this with substantially reduced sample requirement. This supports Theorem 4, which predicts increasing gradient alignment with targeted memory updates, leading to efficient drift recovery. As result, AMR enables efficient adaptation to concept drift without the need for extensive retraining, offering a resource-efficien solution for real-world continual learning scenarios where drift is prevalent.
Task 1
Task 2
Task 3
Task
Task
Clacis
Reducad Feature Dimenzion
10
Reduced Feature Dimension
(a) No concept drift
Task
Task
Task
Drift Occurs
Task
Reduced Fosture
Reducod Dimension
(b) Concept drift at task 3
Figure 3: Evolution of the task-1 feature space (two classes) across five tasks on CIFAR-10, Without drift (top) the classes remain linearly separable; with drift introduced task 3 (bottom) the features collapse.
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CPRR12 C (PAL1:0
Oriico GRARc
FA
CEPAIDO
FR
Actuncy
Reletium Sample Cant better)
epoch
(a) Relative sample cost vs. accuracy
(b) Relative time cost. vs. accuracy
(c) GFLOPs vs. accuracy
Figure 4: Comparison of computational cost and accuracy for different drift adaptation strategies. AMR achieves wear-equivalent accuracy to with significantly lower sample requirement, relative time and GFLOP consumption.
Buffer S00
Buffer 5000
Buffer 500
Buffer 5000
ER
Drft Dritt
ER
Drirts Drifts
ERACCE
ER-ACE
DER
DER
Accurac/ (%6)
Accurscy (%)
$200
5000
15
560
Bc Acestatii
Adaptaaiion
Adustadion
No Adactaton
Samples required per class for adaplation
Samples required per class for adaptation
(a) CIFAR10
(b) CIEAR100
Figure 5: Comparison of class-incrementll accuracy across different experience replay methods and varying number of drifts using No Adoptation, AMR, and Full Relearning strategies. AMR consistently achieves comparable accuracy to FR while using significantly fewer labeled samples.
5.2 Experimental Results
We conducted series of experiments on Fashion-MNISTT-CD, CIFARIO-CD, CIFAR100-CD, and Tiny- ImageNet-CD to evaluate the effectiveness of our proposed method under concept drift. The experiments varied buffer sizes (IM]= = 500 and 5000) and included both single and multiple drift scenarios. An overview of the experimental outcomes provided in Tables
and
Figures i and present the results on shorter task stremms from Tables 1 and 2 on Fashion-MNIST-CD and CIFARIO-CD under one and two drift events. Across all settings, both the AMR and FR strategies consistently restore model performance following drift(s). These results confirm that the proposed AMR strategy can match the performance of FR. Interestingly we observe that larger buffers exacerbate the impact of concept drift. We hypothesize that smaller buffers, due to stronger forgetting, reduce reliance on outdated representations, forcing the model to adapt more aggressively to new data. In contrast, larger buffers retain older samples longer, potentially hindering adaptation by reinforcing outdated features. This observation reveals an intriguing insight that while larger buffers improve performance static continual learning settings, they may require adaptive mechanisms like AMR to remain effective under drift.
To further evaluate generalization, CD be-hhnnhssrks with longer task streams and larger class spaces (Tables g and 4). We simulate two and four drift events, each of which changes the representations of all previously seen classes. Figures I0 and 12 show that, while FR consistently restores full performance, AMR achieves comparable accuracy and reliably outperforms the Ad-atttanio baseline.
test approach CIFAR-100-CD and Tiny-ImageNer
On large datasets such Tiny-Image Siny-ImageNet-CU forgetting so pronounced that a small buffer cannot support meaningful recovery. Table 3 confirms that 500-sample buffer suffices for CIFAR-100-CD. However, from
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Table Final Average Accuracy (FAA(t)) and Forgetting (F(4) for Pashion-MNIST- (3-run average).
Vanilla Baseline without Drift Adaptation,
FR
Putt Relearning, AMR
Adeption Memory Realigmment
S-PMIIST-CD
M
Method
Adaptation
FAAtte (FL)
No Drift
FAATaad (F4)
Drff
FAAt (FL)
2 Drifts
Vanilla
71.5+1127 (22.86)
63.01=0.60 (36.69)
51.6+00.00 (55. 03)
ER
FR
84.2620.49 (18.27)
82.231. 18 (20.50)
AMR
96.30=0.56 (15.88)
78.80+0 34 (24.79)
Vanilla
82.77+0.14 (8.32)
73.00-1.67 (29.71)
63.13+1.20 (31.75)
ER-ACE
FR
87.8320.16 (1.40)
89.65.20.50 (5.49)
AMR Vanalla FR
88.67 (6.01)
85.1110.35 (12.80)
81.920.07 (14.07)
00.69 (27.07)
60.13+1.37 (40.40)
+ER++
89.13+0.42 (7.93)
74.39+0.41 (26.78)
AMR Vanilla FR
88.28 20.50 (10.27)
79.86+10.15 (18.93)
8138+0.47 (13.19)
70.32=0.59 (26.39)
63.184143 (31.82
SER
86.86+0.15 (7.38)
29.37 (3.40)
AMR Vanalla 79.9820.72 (18.82)
89.392..11 (6.04)
80.2540.00 (16.68)
08.1321.66
(33.45)
57.3411.4 (46.85)
CLS-ER
FR
76.52=0.29
(21.11)
85.82+0.2 18 (12.93)
AMR 80.6220.97 (17.66)
76.37+0.61
(25.30)
79.11 (21.28)
Vanilla
62.5121.20
(40.55)
58.7910 32 (48.08)
ER
FR
85.11=1.17
(10.24)
90.7642047 (7.89)
AMR Vanilla 17 (3.58)
84.51-0.e4
(14.19)
92.48+0.12 (6.80)
74.89=0.c5
(18.04)
58.51+1.00 (39.78)
ER-ACE
FR
89.23+a.ra
(0.84)
90.40+0.67 (4.41)
AMR
93.39:20.1 (2.75)
93.17:t0.11 (4.25)
Vanalla 27.00 (5.88)
74.2721.16 (23.23)
62.6511.16 (37.55)
5000
DER++
FR
90.70 61 (4.83)
91.23:10.07 (1.44
AMR Vanalla FR
91.82+0.25 (5.73)
87.72+0.77 (10.98)
87.50=0022 (3.78)
72.86=0.57 (22.27)
66.19 (30.83)
SER
89.05+ 48 (1.14)
90.66:+0.24 (3.54)
AMR
91.39+a..1 (6.13)
91.51+0.23 (4.98)
Vanilla
78.1721.5 (22:24)
59.91
(43.87)
59.8011.48 (44.19)
CLS-ER
FR
87.3720.15 (10.93)
85.4810 (11.34)
AMR
87.41=0.45 (11.92)
80.49+0.44 (20.93)
Table 2: Final Average Accuracy (FAA(t)) and Forgetting (F(4)) for Split-CTFARIO-CD (3-rum average).
Vonilla = Baseline without Drißt Adaptation, = Relearning, AMR Adoptive Memory Realignment
S-CIFARIO-CD
No Drift
1 Drift
2 Drifts
M
Method
Adaptation
EAAttad (FL)
FAAt=ad (FI)
FAAf (FL)
Vanilla
34.3710:50 (74.96)
32.3620:51 (77.06)
29.3210.31 (81.53)
ER
FR
56.00=0.c9 (47.00)
46.88 (58.06)
AMR
(52.44)
49.25+0.00 (55.78)
Vanalla
57.3211.10 (29.26)
37.6820.98
(52.40)
30.761:..7 (64.03)
ER-ACE
60.50.0.76
(27.72)
53.9011.21 (37.75)
AMR
60.02=0.67 (31.63)
50.62+0.21 (44.63)
Vanilla
41.4020.52 (63.55)
37.8321.24 (68.14)
30.0710.31 (78.13)
500
DER+
FR
24.8121.24 (35.21)
50.3611.51 (51.62)
AMR
55. 13=1.0 (46.51)
55.36+0.31 (16.55)
Vanilla
(29.73)
(53.20)
32.54+0.70 (63.96)
SER
FR
15.15 (22.05)
59.601..55 (23.66)
AMR
62.5520.8s (32.45)
48.6811.49 (51.50)
Vanalla
28.7810.11 (81.80
20.2020772
(81.05)
26.2010.42 (84.63)
CL.-ERR
FR
54.4620.31
(49.34)
46.4810.73 (58.90)
AMR
64.06=20771
(49.85)
53.4110.54 (50.61)
Vanilla 66.1722054 (33.25)
47.56=0.22
(56.36)
36.13 (71.00)
FR.
FR
75.2720.41
(21.78)
63.2010.55 (36.73)
AMR Vanilla
73.92
(24.33)
65.2740 36 (35.24)
68.76+0.67 (13.86)
11.62-0.44 (46.05)
32.37+0.4 (59.81)
ER-ACE
FR
71.07=0.445 (15.28)
61.31+a.77 (27.70)
AMR
73.58
(15.72)
65.274. (28.43)
Vanalla FR
65. (20.63)
40.6820.35
(52.06)
35.2510 23 (67.999
5000
DER++
75.76
(19.28)
64.124..77 (31.77)
AMR
70.65_1.15
(26.54)
64.24
(35.23)
Vanalla
69.2220.33 (15.20)
10.73=0.44
(13,97)
33.95 40 (58.70)
SER
FR
72.74-0.78
(12.13)
63.54+0.35 (23.94)
AMR
72.96=20.45
(17.36)
59.05 ss (39.98)
Vanalla
00.8720:17 (33.13)
40.00520117
(55.47)
36.31
(71.77)
CLS-ER
FR
76.2720.11
(21.16)
64.594 30 (34.99)
AMR
77.2020.42 (20.18)
67.5810.40 (32.15)
Table it is evident that several methods fail recover on Tiny-ImageNet-OO with the same capacity. We therefore recommend buffer size of 5000 for effective drift adaptation large-scale datasets.
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Table 3: Final Average Accuracy (FAA(t)) and Forgetting (F(4) for Split-CIFAR100 CD (3-run average).
Vanilla = Baseline without Drift Adaptation,
FR
Pull Relearning, AMR
Adeption Memory Realigmment
S.CIFARIO0-CD
M
Method
Adaptation
FAd (FL)
No Drift
PAAT (F
Drsfts
Drifte FAAt (FL)
Vanilla
9.71=0.18 (71.80)
9.15+0.00 (7194)
8.70=0.07 (75.58)
ER
FR
12.1520.30 (69.16)
13.71 a5 (60.74)
AMR
12.37=0.34 (70.55)
12.79+0.25 (09.96)
Vanilla
21.95+036 (39.40)
12:37+0:24 (48.92)
7.05++0.35 (35.04)
ER-ACE
FR
18.06:0.32 (43.28)
16.2410.46 (49.96)
AMR
16.29
(45.09)
11,8710 (51.70)
Vanalla FR
12.11=0.07 (69.80)
10.38=0.10
(72.03)
8.90=0.04 (74.33)
+ER++
(63.39)
AMR Vanilla 26.60+0.28 (12.38)
13.57=0.25
(67.61)
12.02+0.11 (68.91)
18.62=0.11
(51.06)
12.29+ (58.29)
SER
FR
20.71
(41.83)
22.00+:044 (42.04)
AMR Vanalla 11.8200.0 (73.10)
17.73-0.71
(46.52)
13.3740.18 (52.77)
10.4020.12
(74.87)
9.38 (76.09)
CLS-ER
FR
18.99=0.14
(62.31)
17.01+ (61.99)
AMR 22.8020.20 (67.28)
17.11+0.11
(66.79)
14.95+0.31
(68.71)
Vanilla
15.7720.43
(64.33)
10.961 14 (60.67)
ER
FR
21.01=0.24 (53.18)
22.05+0.02 (51.66)
AMR Vanilla 32.4120.46 (31.83)
25.71+0.33 (53.43)
24.06+00.20 (55.40)
10.80=0.19 (48.50)
9.80 (36.52)
ER-ACE
FR
27 27.36-0.17 (38.41)
23.33+0.50 (14.47)
AMR
27.29
(39.81)
22.924..34 sa (46.73)
Vanalla 30.851031 (36.13)
15.8120.35 (52.48)
10,4310.00 (58.87)
5000
DER++
FR
27.3420.63 (40.33)
24.4110.11 (44.11)
AMR Vanalla FR
27.71+0.15
(45.62)
22.68+0.21 (51.97)
36.1820.01 (14.16)
17.1920.41
(34.61)
10.96 18 (42.29)
SER
26.79=0.6s
(25.56)
25.33+.0 38 (28.76)
AMR
27.40-00224
(34.61)
20.88+0.55 (46.44)
Vantlla
32.782011 (43:26)
19.3320.12 (57.74)
12,8331 18 (64.49)
CLS-ER
FR
32.0620.05
(42.17)
26.471.0.1 (48.64)
AMR
31.47=0.31 (44.51)
26.75+0.37 (51.58)
Table 4: Final Average Accuracy (FAA(t)) and Forgetting (F(J)) for Split-TnnyImageNees (3-rum average).
Vanilla = Baseline without Drißt Adaptation, FR = Relearning, AMR Adoptive Memory Realignment
TTiny-ImageNettcC
No Drift
Drifts
4 Drifts
MM
Method
Adaptation
EAAttad (FL)
FAAtaad (FI)
FAAf (FL)
Vanilla
6.2220-11 (57.50)
6.2511.15 (58.50)
6.3020.03
(58.16)
ER
FR
6.6420.10 (55.21)
6.4820.17 (35.67)
AMR
6.1940.15 (57.67)
(37.17)
Vanalla
10.7620.1 (32.76)
5.00 (37 99)
2.5620.05 (41.23)
ER-ACE
6.001 (39.87)
5.4620.25 (44.64)
AMR
0.96+ (38.40)
3.1920.11 (41.38)
Vanilla
6.61 (38.85)
6.4510.12 (58.67)
6.44200.7 (59.03)
500
DER+
FR
7.0410.18 (48.77)
7.9720.11 (34.27)
AMR
6.65+ (56.54)
6.20=0.08 (37.37)
Vanilla
(21.69)
(27.80)
(32.29)
SER
FR
11.71 (26.65)
8.7120.33 (31.77)
AMR
7.3240.28 (28.07)
4.7320.92 (26.84)
Vanalla
6.50=0007 (57.30)
6.30 (57.60)
6.1620.27 (57.211
CLS-ERR
FR
7.8710.01 (53.70)
8.7220.03 (53.56)
AMR
7.4640.18
(56.56)
7.30 (36.29)
Vanilla 2.91 (58.40)
8.30
(60.74)
7.1220.14 (61.27)
FR.
FR
9.6911.05
(56.20)
9.3520.17 (56.19)
AMR Vanilla 16.16+0.30 (32.52)
12.0.s (57.80)
8.57 (58.20)
8.60+
(11.78)
ER-ACE
FR
11.71=0.04
(38.26)
9.23=0.05 (45.20)
AMR
11.10:20.17 (41.24)
7.4820.0s (46.96)
Vanalla FR
11.5620.4 (30.43)
6.32210.05 (42.40)
6.3820.15 (43.10)
5000
DER++
10.5220.30
(39.23)
8.82.0.12 (41.06)
AMR Vanalla FR
9.8719.62 (49.14)
8.04 (53.03)
16.2220.40 (10.87)
0. (20.35)
4.8120.29 (23.95)
SER
(20.87)
AMR
10.53=0.22 (23.22)
6.84 (31.33)
Vanalla
16.2310.99 (30.00
9.8T10.11 (46.77)
7.4120.13 (49.77)
CLS-ER
FR
14.4720.18 (42.63)
11.134..11 14 (47.10)
AMR
14.33-2022 (43.24)
10.784.0.30 (48.96)
Beyond accuracy, practical deployment requires minimizing both the number labeled samples and the com- putational cost of adaptation. To quantify these trade-offs, compared the two approaches across three key
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ER
ER-ACE
+ER++
SER
CLS-ER
Accuracy (%6
90
73
70
30
Vanila
30
29
33
Drift: Vanilla
Drift FR
Drift:
Task Trained On
ER
ER-ACE
DER+
SER
CLS-ER
Accuracy (%6
30
Drift. Vanilla
Drift: AMR
Task Trained On
Figure 6: class-incremental accuracy on S.ashionMNIST-CD with a single drift event occurring at task 3. Results are shown for buffer sizes 500 (top) and 5000 (bottom).
ER
ER-ACE
DER+
SER
CLS-ER
Accuracy (%))
80-
33-
Drift: Vanilla
Drifts:
Drift::
Task Trained On
ER
ER-ACE
DER+
SER
CLS-ER
Accuracy (%6)
50
33
2 Drifts: Vanilla
30
Drits: AMR
33
30
Task Trained On
Figure 7: Class-incremental accuracy on S.FashionMMNIST-CDD with drift events at tasks 4. Results are shown for buffer sizes 500 (top) and 5000 (bottom).
metrics: time per epoch, GFLOPs, and the number of labeled samples required for adaptation. All metrics were normalized with respect to the FR baseline, which is assigned a normalized value 1.0 (representing the highest cost). The performance of AMR expressed on a 0~1 scale, where lower values indicato better effi- ciency relative FR. As shown in Figure [3. AMR consistently requires fewer computational resources and labeled samples across FashionMNIST-CDD CIFAR10-CD, CIFAR100-CD, and Ting- datasets.
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ER
ER-ACE
+ER++
SER
CLS-ER
130
150
Accuracy (%)
70
Vanill
20
Drift: Vanilla
23
30
Drift: FR
Drift: AMR
Task Trained On
ER
ER-ACE
DER+
SER
CLS-ER
130
100
Accuracy (%6
90-
93
Drift: Vanilla
24-
Drift AMR
Task Trained On
Figure innsemnncreenttl accuracy on S-CIFARIO-CD with a single drift event occurring at task 3. Results are shown for buffer sizes 500 (top) and 5000 (bottom).
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Figure 9: Class-incrrmenntll accuracy on S-CIFAR10-CD with drift events al tasks 2 and 4. Results are shown for buffer sizes 500 (top) and 5000 (bottom).
As expected, these results confirm that although effective at mitigating concept drift, the FR strategy incurs significant overhead, as it requires full retraining on large labeled datascts following cach drift event. In contrast, AMR's selective replacement of only outdated entries in the memory bulfer avoids unnecessary retraining and reduces the overall adaptation cost. This makes AMR not only competitive in terms of accuracy, but also significantly more efficient in both computational and labeling costs. By operating well below the resource demands of Full Relearning, AMR presents a practical and scalable solution for real-world continual learning under concept drift.
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Figure 10:
Class-incrementar accuracy on S-CIFAR100 with drift events at tasks and 7. Results are shown for
buffer sizes 500 (top) and 5000 (bottom).
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Figure 11: Class-incremental accuracy on S-CIFAR100 with drift events at tasks 3, 5, 7, and Results are shown for buffer sizes 500 (top) and 5000 (boltom).
6 Conclusion, Limitations, and Future Works
We propose a novel continual learning scenario in which adaptation is required not only for newly arriving classes, but also for previously learned classes whose representations evolve over time due concept drift. To address this setting, we introduce holistic framework that couples drift detection with drift-aware memory management, allowing rehearsal- bhased learners both retain stable knowledge and rapidly revise outdated concepts. Concretely, Adaptive Memory Realignment (AMR) mechanism
Conclusion:
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ER
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Figure 12: Class-incresd class-incremental accuracy on S-Tiny-mmageNet-CO CD with 5000 buffer size.
AM
3,75
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(d) Lmn--ImageNet-CD
Figure 13: Radar plots comparing the efficiency AMR and FR across normalized metrics: time per epach, GFLOPs, and labeled samples required. All values are normalized such that FR 1.0 (maximum cost), with lower values indicating better efficiency. AMR consistently shows lower resource requirements across dimensions.
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selectively flushes stale buffer samples of drifted classes and repopulates those slots with small number of up-to-date labeled instances, thereby realigning rehearsal gradients with the current data distribution. Across four drift- anftt-uugmentd vision benchmarks Tin--mmagNNet-CD and a real temporal-drift setting (CLEAR-10), AMR consistently recovers performance after drift. while preserving accuracy on non-dristed tasks and does so with dramatically lower labeling and computational costs than full relcarning from scratch. These findings highlight that "remembering" in continual learning is insufficient in non-stationary environments: robust agents must also detect when stored knowledge becomes stale and selectively update it. More broadly, by providing both reproducible evaluation framework and strong empirical evidence for lightweight memory realignment, our work helps bridge continual learning and data-stream mining perspectives and encourages future research on end-to-end systems that jointly detect, diagnose, and adapt to diverse drift regimes in long-running deployments.
CIFARIO-CID CIFARI00-CD,, and
Limitations: Our framework currently relies on an external drift detector to trigger adaptation; thus, its end-to-end robustness is bounded by the detector's accuracy, calibration, and assumptions under distribution shift. In particular, false negalives can delay adaptation, leading to prolonged performance drops, while false positives can induce unnecessary memory realignments and additional labeling/computte compute overhead. More- over, our specific instantiation (uncertaint--disribbbtto testing against a buffer-derived reference) inherits practical limitations: (i) the reference distribution can be noisy when the buffer is small, highly imbalanced across classes, or already partially contaminated by earlier undetected drift; and (ii) some shifts may not manifest as a clear change in predictive uncertainty, reducing detector sensitivity. Beyond detector depen- dency, AMR may exhibit degraded performance in challenging drift scenarios, such as:
Adversarial drift concealment, where adaptive adversary smooths or masks distribution shifts to delay detection and retain corrupted samples in the buffer.
Gradual or mixture drift, where overlapping pre- and post-drift subdomains produce weak, intermittent, or oscillatory detection signals, causing delayed or excessive reactions.
Open-set recurrence / novel modalities, where previonsly seen classes reappear with new internal modalities or semantically related variants; in such cases, AMR may misinterpret semantic novelty as distributional drift (or vice versa), and naive full replacement may discard still-relevant sub-modes.
Finally, our study focuses on rehearsal-based continual image classification; while AMR is modcl-agnostic once drift is flagged, we do not claim jointly optimized detection-adaptatti pipeline, nor do we com- prehensively evaluate extensions to non-rehearsal paradigms or other problem types, where drift signals and memory semantics may be substantially different. We therefore view detector choice, calibration, and supervision constraints as key practical considerations that must be tailored to the target application.
Future Works: Several research directions emerge from this work. First, hybrid detection strategies that combine multiple drift signals (e.g,, uncertainty-basee tests, feature-space divergence measures, and reconstruction errors) could improve robustness across diverse drift regimes, reducing both false positives and false negatives. Second, semantic drift monitoring that tracks class-level feature evolution rather than relying solely on distributional tests could better distinguish mcaningful reprosentation shifts from minor perturbations, minimizing unnecessary memory updates. Third, exploring learned or self-tuning drift de- tectors that adapt their sensitivity based on observed stream characteristics represents a promising avenue for reducing manual threshold calibration. Fourth, alternative pre-training strategies such as self-supervised temporal contrastive learning could yield detector features better suited to capturing gradual distributional evolution compared to maageNet-prettainne representations optimized for classification. Finally, exlending AMR to handle open-set recurrence and novel subclasses, where previously seen classes reappear with new internal modes or semantically related but novel classes emerge, would enhance applicability to more com- plex world scenarios. Collectively, these directions aim to develop more autonomous, adaptive continual learning systems capable of robust long-term deployment in non-stationary environments.
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A Justification for Concept Drift Transformation
Figure 14 shows the effects of various transformations on recurring classes in CIFAR1O, using vanilla Expe- rience Replay (ER) with a bulfer size of 5000. Specifically, during tasks 2 and 4, previously learned classes reappear alongside new classes, with the recurring ones modified using the indicated transformations to induce concept drift. The figure shows that certain transformations have a more pronounced impact than others. Defocus blur and shot noise fail to produce meaningful representation shifts, even their highest severity levels. In contrast, Gaussian noise, rotation, and permulation significantly degrade performance, indicating stronger representation drift.
Deffccu Blur Drift
Shot Noise Dritt
Gaussian Noise Drift
Rotattion Drift
Permutation Dritt
100
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onft: 65.78%
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drfit 65.78%
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sevenly 52%
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seperity-1: 1: 84%
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severiy 89 30%
severfty2:
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Seowelly 45 89%
36
Task Trained On
Figure 14: Impact of concept drifts induced by image transformations of varying severity at lasks 2 and 4 on CIFARIO
B Real-World Drift Experiments on CLEAR Dataset
To evaluate AMR's effectiveness natural temporal distribution shifts beyond synthetic transformations, we conduct experiments on the CLEAR-10 dataset (Lin et al. 2021 which captures real-world visual concept evolution from 2004 to 2014.
Experimental Setup: We construct a 5-task class-incremental scenario using CLEAR-10, excluding the BACKGROUND class to maintain a 10-class setting. Each task introduces classes drawn from temporal buckets 1-2 (approximately 2004 2005-.00). Since each class contains only 3300 training samples per bucket, we combine two consocutive buckets per class to increase sample counts. For non-drifted scenarios, training and test sets use the same temporal buckets to maintain distribution consistency. To induce drift, we replace the test distributions of recurring classes with samples from temporal buckets 9-10 (approxinately 2012-2014 creating natural temporal shifts spanning 8- 1010 years. We evaluate ER, ER-ACE, and CLS-ER with a buffer size of [M = 1000 under a 2-drift sconario. DER++ and SER were excluded due to memory constraints on CLEAR-10.
During implementation, we identified an important limitation of uncertainty- based drift detection on gradual temporal shifts. Our original KS-test on predictive uncertainty (p 0.05) detected drift in only ~50% of temporal shift scenarios. Relaxing the threshold to = 0.2-033 yielded marginal improvement while compromising statistical significance. To address this, we integrated the Maxi- mum Mean Discrepancy (MMD) drift detector (Gretton et al., 2012), which operates in feature space rather than output uncertainty space. MMD measures distribution distance in a Reproducing Kernel Hilbert Space by comparing expected feature embeddings between source and target distributions. This feature-level com- parison proved significantly more sensitive to subtle visual evolution across temporal buckets, achieving near-perfect drift detection CLEAR-10.
Drift Detection Challenges:
Figure 15 presents the results on Split-CLEAR1O-CD. Unlike the synthetic drift benchnarks in the main paper, natural temporal shifts in CLEAR-10 produce subtle distribution changes. Vanilla adaptation shows minimal degradation between no-drift. and 2-drift scenarios, indicating that 8-10 years of temporal evolution creates gentler drift than synthetic transformations such as permutation.
Results and Discussion:
Table 5ishows that despite the subtle drift signal, AMR demonstrates consistent improvements on ER, achiev- ing +7.17% FAA over Vanilla and +3.97% over FR. For CLS-ER, AMR slightly outperforms FR performance
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ER
ER-ACE
CLS-ER
100
100
100
Accuracy (%6
53
50
40
No Drift. Vanilia
40
40
Vanilla
30
Drifts: FR
30
30-
Dritts: AMR
20
20
20
Task Trained
Figure 15: Class-incremental accuracy on SpLi-CCLEARIO-CD with buffer size 1000. Drift events occur at tasks 2 and 4, where recurring classes are replaced with samples from temporal buckets 9-10 (2012-2014). Natural temporal drift produces subtler distribution shifts compared to synthetic transformations, resulting in smaller performance gaps between adaplation stralegies.
(55.30% vs. 55.27%). However, on ER-ACE, FR outperforms AMR (59.33% vs. 53.00%), suggesting that ER-ACE's asymmetric cross-entropy loss may interact differently with MMD-triggered realignment under subtle drift conditions.
Table 5: Final Average Accuracy (FAA(t)) and Forgetting (F(4)) for SpER--CLERRIOCDD (3-rum average).
Vanilla Baseline without Drift Adaplation, FR Full Relearning, AMR Adaptive Memory Realignmend
M
Melhod
Adaptation (# drifls)
FAAt
F
Vanilla (0)
45.0021.15
57.54
Vanilla (2)
44.33
51.92
ER
FR (2)
47.53
52.92
AMR (2)
51.50+2.06
50.33
Vanalla (0)
10.93
29.92
Vanilla (2)
49.58 1.11
28.00
1000
ER-ACE
FR (2)
39.3320.99
25.92
AMR (2)
53.00+0.80
28.96
Vanalla (0)
47.37 20029
53.62
Vanilla (2)
46.47
55.12
CLS-ER
FR (2)
35.2743.00
38.00
AMR (2)
55.30
42.92
These results reveal important insights about the relationship between drift magnitude and adaptation strategy effectiveness. While AMR excels at recovering from pronounced synthetic drifts, its advantages are attenuated when drift signals are subtle and gradual. This aligns with the limitations discussed in Section 6: gradual drift produces weak detection signals that can cause suboptimal adaptation timing. Nevertheless, AMR remains competitive with or superior to FR across most settings while requiring substantially fewer labeled samples, confirming its utility even challenging real-world drift sccnarios.
C Backbone Justification and Robustness Across Architectures
To validate that AMR's effectiveness generalizes beyond ResNet-18, we conducted additional experiments using deeper convolutional (ResNet-152) and transformer-hased (ViT-S) architectures. We evaluate Fx-
28

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Published in Transactions on Machine Learning Research (01/2026)
perience Replay (ER) S-CIFARIO-CD with 2 drifts and S-CIFAR100-D with 4 drifts under identical conditions (permutation drift, bulfer size [ 5000).
ResNatt1152
Reshitt1152
Drfh Vants
Errfo Vanila
Alcuracy US6
63-
AcCuUSSI 1%
30
N: Cefc ln
Task Trained
Taskk Trained
(a) CIFARIO
(b) CIFARI00
Figure 16: Class incremental accuracy using ResNet-1152 and ViT-$ backbones with Experience Replay (ER) and
buffer size 5000. Drift events occur at tasks 2 and 4 for CIFAR10, and tasks 3, 5, 7, and 9 for CIFARI00.
Table 6: Final Average Accuracy (FAA(t)) and Forgetting (F(J)) across different backbone architectures.
Dataset
Backbone
Adaptation (# drifts)
FAAt
Fl
Vanilla (0)
56.97
41.04
Vanilla (2)
34.87
69.45
ResNet- 152
FR (2)
56.26
42.20
AMR (2)
60.23
38.30
S- C-FFARIOCD
Vanilla (0)
41.91
50.54
Vanilla (2)
30.04
65.21
VIT-S
FR (2)
53.26
37.14
AMR (2)
56.15
35.35
Vanilla (0)
16.80
52.17
Vanilla (4)
9.37
60.71
ResNet-152
FR (4)
19.28
48.98
AMR (4)
19.56
50.53
S-CIFAR100-CD
Vanilla (0)
17.53
45.94
Vanilla (4)
9.15
54.69
ViT-S
FR (4)
19.86
44.14
AMR (4)
20.37
47.48
As shown in Table G and Figure 16 AMR consistently outperforms both the Vanilla baseline and Full Re- learning (FR) across both convolutional and transformer architectures. On S-CIFAR10-CD, AMR achieves gains of +3.97% FAA over FR with ResNet-152 and +2.89% with VIT-S. The performance trends ob- served with ResNet-18 in the main experiments are preserved across architectures, confirming that AMR is architecture-agnostic. The choice of ResNet-18 for the main experiments was motivated by computational efficiency, enabling comprehensive evaluation across multiple datascts, buffer sizes, and drift sccnarios while maintaining tractable training times.
Hyperparameter Selection
Abbreviations: mb = mini- batch size, bs batch size, regw = regularization weight, sm sm__f uf = stable model update frequency, pm_ uf = plastic model update frequency
29

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Published in Transactions on Machine Learning Research (01/2026)
Table 7: Hyperparameters for S-FMNIST-CD and S-CIFARIO-CI
Huperpperameters
Method
M
S-FMNIST-CL
8-CIFARIO-CD
500
0.1, mbb 10, 10, epochs:
0.1, mbb 32, bs: 32, epochs:
ER
5000
Ir: 0.1, mb: 10, bs: 10, epochs:
Ir: 0.1, mb: 32, bs: 32, epochs: 50
500
Ir: 0.03, mbb m/. 10, bs: 10, epochs:
Ir: 0.03, mbb: 32, bs: 32, epochs: 50
ER-ACE
5000
Ir: 0.03, mnh 10, bs: 10, epochs:
Ir: 0.03, mub: 32, bs: 32, epochs: 50
500
Ir: 0.1, 10, bs: 10, a: 0.2, 3: 0.5, epocles: 1
Ir: 0.03, melr: 32, bs: 32, 0.2, 8: 0.5, epoohs: 50
DER+
5000
0.1, 10, bs: 10, a: 0.2, 3: 0.5,
epochs: 1
0.03, rebe 32, bs: 32, as 0.1, 8: 1.0, epocks:
500
0.1, mb: 10, bs: 10, a: 0.2, 3: 0.2, epochs: 1
0.08, mb: 32, ba: 32, o: 0.2, 8: 0.2, epochs: 50
SER
5000
Ir: 0.1, 10, bs: 10, a: 0.2, 3: 0.2, epochs: 1
Ir: 0.03, mb: 32, bs: 32, 0.2, 8: 0.2, epochs: 50
Ir: 0.1, mb: mb: 10, bs: 10, reg__e_ 1.0, sm sm_uf: 0.9, sma: 0.99, pm_uf uf:
Ir: 0.1, mb: bs: 32, reg_ w. 0.15, sm smmssmmmffff.m
uf: 0.1,
500
pma: 0.99, epochs: 1
smai 0.999, pm_ _u:: 0.9, pmaa: 0.999, epochs: 50
CLS-ER
Ir: 0.1, mb: 10, bs: 10, reg wi 1.0, sm uf: 0.8, sma? 0.99, pm pff 1.0, pma: 0.99, epochs:
Ir: 0.1, mb: 32, bs: 32, reg wi 0.15, sm sm__f: 0.8, sma 0.999, pm_uf: 1.0, pmma: 0.999, epochs:
5000
f:
Table Hyperparameters for S-CIFAR100-CD and S-Timy-ImageNett
Hyperpaarmeeeers
Method
M
S-CIFAR100-CD
S- Teny-n Mengg-mmaeeNet--ooo CD
0.1, rub: 32, bs: 32, epochs:
0.03, wad: 32, bs: 32, epochs:
ER
5000
Ir: 0.1, mb: 32, bs: 32, epochs: 50
Ir: 0.1, mb: 32, bs: 32, epochs: 100
500
Ir: 0.03, mb: 32, bs: 32, epochs: 50
Ir: 0.03, mbb: 32, bs: 32, epochs: 100
ER-ACE
5000
Ir: 0.03, md: 32, bs: 32, epochs: 50
Ir: 0.03, mbd: 32, bs: 32, epochs: 100
500
Ir: 0.03, mb: 32, bs: 32, 0.1, 3: 0.5, epochs: 50
Ir: 0.03, mdd: 32, bs: 32, 0.2, 3: epochs: 100
DER++
5000
0.03, mb: 32, bs: 32, 0.1, 3: 0.5, epochs:
0.03, mbd: 32, bs: 32, or 0.1, (: 0.5, epochs: 100
500
Ir: 0.03, mb: 32, bs: 32, ac 0.5, 3: 0.5, epochs: 50
Ir: 0.03, mb: 32, bs: 32, a: 0.2, 8: 1.0, epochs: 100
SER
5000
Ir: 0.03, mb: 32, bs: 32, 0.5, 3: 0.5, epochs: 50
Ir: 0.03, mbd: 32, bs: 32, 0.2, 8: 1.0, epochs: 100
Ir: 0.1, mbb 32, bs: 32,reg reg 0.15, sm sm_uf= 0.1,
uf:
Ir: 0.05, mab: 32, bs: 32, reg__w: w:: 0.1, sm___ff
uf: 0.05,
500
sma: 0.999, uuf 0.9, pma 0.999, epochs: 50
sm.: 0.999, pm uf: 0.08, pm.: 0.999, epochs: 100
CLS-ER
Ir: 0.1, meb: 32, bs: 32, reg_ee___ 0.15, swn s/ 0.8, sma, 0.999, pma _us: uf: 1.0, pmn: 0.999, epochs:
Ir: 0.05, wad: 32, bs: 32, reg_eeg__r
w: 0.1, sm__ff 0.07.
5000
syn,; 0.999, pm pmm__uf f 0.08, pma.: 0.999, epochs: 100
30


Reviews:
[{"review_id": "4MKyCU7M0v", "reviewer": "Reviewer_nKVi", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces a framework for continual learning that accounts for concept drift, where the distributions of previously learned classes can change over time. The authors create four new benchmark datasets by applying transformations to existing vision datasets to simulate this drift. They propose a method called Adaptive Memory Realignment (AMR), which works with rehearsal-based continual learning algorithms. AMR uses a drift detector to identify shifted classes and then selectively flushes outdated samples from the memory buffer, replacing them with a small number of new, labeled instances. The main finding is that AMR can match the performance of fully retraining on the new data (Full Relearning) while being significantly more efficient in terms of labeled data and computation.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The claims are generally supported by the empirical evidence provided, but some aspects could be strengthened.\n\nThe central claim that AMR matches the performance of Full Relearning (FR) with substantially lower overhead is well-supported by the results in Tables 1, 2, 3, and 4, as well as the visualizations in Figure 5 and Figures 6-12. For example, in Table 2 for S-CIFAR10-CD with M=5000 and 1 drift, AMR (paired with CLS-ER) achieves 77.20% FAA, which is even slightly better than FR's 76.27%, while requiring orders of magnitude fewer samples and less computation as highlighted in Figure 13.\n\nThe theoretical claim of gradient misalignment (Theorem 1) is intuitively supported by the UMAP visualizations in Figure 3. Figure 3b clearly shows the feature space collapsing when drift occurs, which makes the argument for misaligned gradients plausible. However, the theoretical analysis itself is somewhat high-level and relies on strong assumptions. I have checked the proofs in Section 3.5; they are mathematically straightforward derivations but serve more as a formalization of intuition rather than providing deep, non-obvious insights. For example, the conclusion of Theorem 3, that replacing old samples with new ones maximizes alignment with the new gradient, is almost tautological.\n\nThe evaluation criteria and proposed methods are sensible for the problem. The use of standard rehearsal methods as a base is a good choice, and the FAA and Forgetting metrics are standard in continual learning. The experimental design is thorough in its breadth of baselines and datasets. However, the reliance on a single, fixed, pre-trained model for drift detection across all experiments is a significant methodological choice that is not sufficiently justified or analyzed, potentially limiting the generality of the findings.", "audience_yn": "Yes", "audience_explanation": "The primary strength of this paper is its focus on a more realistic and challenging continual learning scenario. Most existing work assumes static past tasks, which is often not the case in the real world. By explicitly modeling and addressing concept drift at the representation level, the paper tackles an important and underexplored problem.\n\nAnother notable contribution is the creation and open-sourcing of four concept drift benchmark datasets (Fashion-MNIST-CD, CIFAR10-CD, CIFAR100-CD, and Tiny-ImageNet-CD). These benchmarks provide a valuable resource for the community to systematically evaluate and compare methods in this new setting.\n\nThe proposed method, AMR, is simple, lightweight, and intuitive. Its effectiveness is demonstrated through extensive experiments across five different rehearsal-based methods, two buffer sizes, and multiple drift scenarios. The empirical results, particularly the efficiency comparisons shown in Figure 4 and Figure 13, convincingly argue that AMR provides a practical and scalable solution that achieves a favorable trade-off between accuracy and resource consumption compared to the brute-force Full Relearning approach.", "requested_changes": "- The most critical action for improvement is to conduct a thorough literature review covering concept drift adaptation from data stream mining and recent works on adaptive/plastic continual learning. The paper needs to be repositioned in light of this existing work.\n- The authors may add an ablation study in the experimental section to analyze the sensitivity of AMR to the drift detector's parameters (e.g., the KS-test threshold $\\delta$) and its architecture/pre-training data. This would significantly strengthen the empirical claims.\n- Given the reliance on an external, fixed drift detector, the claim of a \"holistic\" framework is too strong. The authors should be more transparent about this dependency and discuss its implications as a limitation.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "XMqHcIF6NN", "reviewer": "Reviewer_vBrW", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "**Summary**\n\nThe paper addresses concept drift in continual learning, where the representation of previously learned classes evolve over time. The paper proposes Adaptive Memory Realignment (AMR), a framework for rehearsal-based methods that selectively removes outdated samples from the replay buffer and replace them with fresh samples to realign the memory with the new data distribution. The proposed framework is evaluated on four simulated (based on image transformations) drift datasets, derived from Fashion-MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet. Compared to the non-adaptive baseline (Vanilla), the proposed framework consistently demonstrates improved accuracy and reduced forgetting when applied to the selected experience replay methods.\n\n**Strengths**\n\n1. The paper is clearly written and easy to follow.\n2. The problem setting of concept drift in continual learning is relevant and important.\n3. The application of an uncertainty-based detector in this problem setting appears to be novel.\n4. The extensive experimental results provide evidence of the empirical merit of the proposed framework.\n\n**Weaknesses**\n\n1. Although the proposed framework is evaluated extensively on four datasets, the concept drifts are simulated using image transformations. As a result, it remains unclear how much empirical benefit the framework would provide when applied to real-world concept drift scenarios.\n2. The presentation of the two metric values in Table 1-4 is unclear. The reader is left to guess that the values in brackets correspond to _Forgetting_.\n3. There is an undefined notation $M_c$ in Algorithm 1 line 11.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The claims are supported by proofs and empirical results presented in the paper.", "audience_yn": "Yes", "audience_explanation": "The paper tackles the problem of concept drift in continual learning which is relevant and important.", "requested_changes": "1. The authors are encouraged to either include additional experiments on real-world drift datasets, such as the CLEAR dataset [1], or explain why the inclusion of such experiments is not applicable or feasible.\n2. In the captions of Tables 1-4, clearly state which values in the table correspond to _Final Average Accuracy_ and which correspond to _Forgetting_.\n3. Define $M_c$ in Algorithm 1, or replace it with the array indexing notation $M[\\cdot]$, which is used consistently in earlier sections of the paper.\n\n**References**\n\n[1] Lin, Zhiqiu, et al. \"The clear benchmark: Continual learning on real-world imagery.\" Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). 2021.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "s91GRx0qSs", "reviewer": "Reviewer_Bxbk", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces a holistic continual learning framework that explicitly addresses concept drift. They propose Adaptive Memory Realignment (AMR), a buffer-update strategy that removes outdated samples and replaces them with new instances. As a result, AMR achieves comparable accuracy compared with full relearning, with lower resource requirements. They have conducted experiments with four drift-augmented benchmarks and show that the AMR consistently restores performance after the drift.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Strengths\n1. AMR effectively maintains performance under concept drift, showing strong adaptation capability.\n2. The evaluation covers four different vision benchmarks, demonstrating applicability across datasets of varying scales and complexities.\n\nWeaknesses\n1. The experiments are limited to a ResNet-18 backbone; effectiveness with larger or more modern architectures (e.g., transformers) remains unclear.\n2. The limitations of AMR are not discussed. For example, what types of drift/data characteristics are handled effectively, and in which cases AMR may fail.\n3. Evaluation is restricted to vision datasets, making it unclear whether AMR generalizes to other domains such as text, tabular, or audio.", "audience_yn": "Yes", "audience_explanation": "Concept drift is a critical issue in continual learning, and the proposed AMR framework provides a resource-efficient solution. The introduction of new drift-augmented benchmarks further increases its relevance, as it provides a foundation for future studies in this area.", "requested_changes": "1. Justify the choice of ResNet-18 as the sole backbone, or include experiments with additional architectures to validate robustness.\n2. Analyze failure cases of AMR in handling concept drift, which would provide valuable insights and future research directions.\n3. Discuss applicability beyond vision, e.g., how AMR could be extended to text, tabular, or audio domains.", "broader_impact_concerns": "No.", "additional_comments": "No.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)