Divide and not forget: Ensemble of  selectively trained experts  in Continual Learning
Authors: Grzegorz Rypeść
Sebastian Cygert
Valeriya Khan
Tomasz Trzcinski
Bartosz Michał Zieliński
Bartłomiej Twardowski
Venue: ICLR 2024 poster (2024)
Keywords: continual learning
class incremental learning
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: Class Incremental Learning, no exemplars, training from scratch. We design an ensemble of experts method, where in each task only one expert is finetuned, but all contribute during inference. We show it is a promising approach for CIL.

Abstract: Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixture-of-expert technique, where different models work together to solve the task. However, the experts are usually trained all at once using whole task data, which makes them all prone to forgetting and increasing computational burden. To address this limitation, we introduce a novel approach named SEED. SEED selects only one, the most optimal expert for a considered task, and uses data from this task to fine-tune only this expert. For this purpose, each expert represents each class with a Gaussian distribution, and the optimal expert is selected based on the similarity of those distributions. Consequently, SEED increases diversity and heterogeneity within the experts while maintaining the high stability of this ensemble method. The extensive experiments demonstrate that SEED achieves state-of-the-art performance in exemplar-free settings across various scenarios, showing the potential of expert diversification through data in continual learning.

Published as a conference paper at ICLR 2024
DIVIDE AND NOT FORGET: ENSEMBLE OF SELECTIVELY TRAINED EXPERTS IN CONTINUAL LEARNING
Grzegorz Rypeść*, Sebastian Cygert!.3, Valeriya Khan Tomasz Trzciíski!
Bartosz Zielinski'. & Bartlomiej Twardowski 1,,7
IDEES-NCBR, ?Warsaw University of Technology. Gdańsk University of Technology,
4 'Tooploox," Jagiellonian University, CCmppter Vision Center, Barcelona
7 Department of Computer Science, Universitat Autònoma de Barcelona,
babastian... cygert, valeriya. khan, tomasz. trzc inski, bartosz. zielinski, bartlomieg
ppl
ABSTRACT
Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know, A trend in this area is to use a mixture-of-expert technique, where different models work together to solve the task. However, the experts are usually trained all once using whole task data, which makes them all prone to forgetting and increasing computational burden. To address this limitation, we introduce novel approach named SEED. SEED selects only one, the most optimal expert for considered task, and uses data from this task fine-tune only this expert. For this purpose, each expert represents each class with Gaussian distribution, and the optimal expert is selected based on the similarity of those distributions. Consequently, SEED increases diversity and heterogeneity within the experts while maintaining the high stability of this ensemble method. The extensive experiments demonstrate that SEED achieves state-of-the-arr performance in exemplar-fres settings across various scenarios, showing the potential of expert diversification through data in continual learning.
INTRODUCTION
In Continual Learning (CL), tasks are presented to the learner sequentially as a stream of non-i.i.d data. The model has only access to the data in the current task. Therefore, it is prone catastrophic forgetring of previously acquired knowledge (French 1999 McCloskey & Cohen 1989). This effect has been extensively studied in Class Incremental Learning (CIL), where the goal to train the classifier incrementally and achieve the best accuracy for all classes seen so far. One of the most straightforward solutions to alleviate forgetting is to store exemplars of each class. However, its application is limited, e.g., due to privacy concerns or in memory-constrained devices (Ravaglia et al. 2021). That why more challenging, exemplar-free CIL solutions attract lot attention.
Many recent CIL methods that do not store ex-
80
Joint
emplars rely on having a strong feature extractor right from the beginning of incremental leamn- ing steps. This extractor is trained on the larger first task, which provides substantial amount of data (i.e., 50% of all available classes) (Hou
E
Inc. Accuracy
60
30
40
al. 2019 Zhu al. 2022 Petit al., 2023
or it starts from a large pre-trained model that remains unchanged (Hayes Kanan 2020a;
Finetune
Frozen
EWC
LWF (A=1)(11)
LWF yA= (0-1 101
PASS(211)
(12A(211)
Fetril/ SSRE(222)
33)
SEED(Gurs)
Wang et al. (222cc that eliminates the problem of representational drift (Yu et al., 2020). How- ever, these methods perform poorly when little training data is available upfront. In Fig. [ we illustrate both CIL setups, with and without the more significant first task. The trend is evident when we have lot of data in the first task re- sults steadily improve over time. However, the
Pub. year
10 classes in the first task, and 10 tasks of classes each
50 classes in the first task, and 10 tasks of 5 classes each
Figure I: Exemplar-free Class Incremental Learn- ing methods evaluated on CIFAR divided into eleven tasks for two different data distributions.
"Code: https: /ttp:///ttttttttu htt.t/ttww.cccmg...////////

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progress is not evident for the setup with equal splits, where frozen (or nearly frozen by high regularization) feature extractor does not yield good results. This setup is more challenging as requires the whole network to continually learn new features (plasticity) and face the problem of catastrophic forgetting of already learned ones (stability).
One solution for this problem is architecture-based CIL methods, notably by expanding the network structure beyond a single model. Expert Gate (Aljundi et al., (017)) creates a new expert, defined as a neural network, for each task to mitigate forgetting. However, it can potentially result in unlimited growth in the number of parameters. Therefore, more advanced ensembling solutions, like CoSCL (Wang et al.) 2022b), limit the computational budget using a fixed number of experts trained in parallel to generate features ensemble. In order to prevent forgetting, regularization is applied to all ensembles during training a new task, limiting their plasticity. [Doan et al. (2022) propose ensembling multiple models for continual learning with exemplars for experience-replay To perform efficient ensembling and control number of the model's s parameters, they enforce the model's connectivity to keep several ensembles fixed. However, exemplars are still necessary, and in CoSCL, task-id is required during the inference.
As remedy for the above issues, we introduce novel ensembling method for exemplar-free CIL called SEED: Selection of Experts for Ensemble Diversification. Similarly to CoSCL and (Doan et al., 2022). SEED uses fixed number of experts in the ensemble. However, only single expert is updated while learning a new task. That, in turn, mitigates forgetting and encourages diversification between the experts. While only one expert is being trained, the others still participate in predictions. In SEED, the training does not require more computation than single-model solutions. The right expert for the update is selected based on the current ensemble state and new task data. The selection aims limit representation drift for the classifier. The ensemble classifier uses multivariate Gaussian distribution representation associated with each expert (see Fig. 2 At the inference time, Bayes classification from all the experts is used for a final prediction. As a result, SEED achieves state-of-the art accuracy for task-aware and task mask-agnostic scenarios while maintaining the high plasticity of the resulting model under different data distribution shifts within tasks.
In conclusion, the main contributions of our paper are as follows:
We introduce SEED, a new method that leverages an ensemble of experts where a new task is selectively trained with only a single expert, which mitigates forgetting, encourages diversification between experts and causes no computational overhead during the training.
We introduce a unique method for selecting an expert based multivariate Gauss distributions of each class in the ensemble that limits representational drift for a selected expert. At the inference time, SEED uses the same rich class representation to perform Bayes classification and make predictions in a aano-agn way.
With the series of experiments, show that existing methods that start CIL from strong feature extractor later during the training mainly focus on stability. In contrast, SEED also holds high plasticity and outperforms other methods without any assumption of the class distribution during incremental learning sessions.
RELATED WORK
Learning (CIL) represents the most challenging and prevalent scenario in the
field of Continual Learning research (Van de Ven & Tolias 2019 Masana et al. 2022). where during the evaluation task-id is unknown, and the classifier has to predict all classes seen so far. The simplest solution to fight catastrophic forgetting in CIL is to store exemplars, e.g. LUCIR (Hou et al., 2019)). BiC (Wu et al. 2019), Foster (Wang et al. 2022a). WA (Zhao et al., (222)). Having exemplars greatly simplifies learning cross-task features. However, storing exemplars can not always be an option due to privacy issues or other limitations. Then, the hardest scenario exemplar-free CIL is considered, where number of methods exists: LwF (Li & Hoiem 2016). SDC (Yu et al. (020), ABD (Smith et al. 2021] PASS (Zhu et al. 2021 IL2A (Zhu et al. 202 SSRE (Zhu et al., 2022). FeTrIL (Petit et al. 2023). ). Most of them favor stability and alleviate forgetting through various forms of regularization applied to an already well- well-performing feature extractor. Some approaches even concentrate solely on the incremental learning of the classifier while keeping the backbone network frozen (Petit et al.)
2023). However, freezing the backbone can limit the plasticity and not be sufficient for more complex
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910
Expert
91
log-likelihood softmax
mean
Expert 2
9t c f(x)
Figure 2: SEED comprises K deep network experts gk f (here K 2). sharing the initial layers for higher computational performance. f are frozen after the first task. Each expert contains one Gaussian distribution per class e E C' in his unique latent space. In this example, we consider four classes, classes and 2 from task 1 and classes 3 and 4 from task 2. During inference, we generate latent representations of input for each expert and calculate its ligliikessoods for distributions of all classes (for each expert separately). Then, softmax those log- log-likelihoods and compute their average over all experts. The class with the highest average softmax is considered as the prediction.
settings, c.g., when tasks are unrelated, like in CTrL (Veniat et al. 2020). This work specifically aims exemplar-free CIL,, where the model's plasticity learning new features for improved classification is still considered an essential factor.
Growing architectures and ensemble. Architecture based methods for CIL can dynamically adjust some networks' parameters while learning new tasks, i.e. DER (aan et al., 2021). Progress and Compress (Rusu et al. 2016) or use masking techniques, e.g. HAT (Serrà et al. 2018). an extreme case, each task can have dedicated expert network (Aljundi et al.) 2017) class (van de Ven et al. 202I). That greatly improves plasticity but also requires increasing resources the number of parameters increases. Additionally, while the issue of forgetting is addressed, transferring knowledge between tasks becomes a new challenge. A recent method, CoSCL, (Wang et al. 2022b), addresses this by performing an ensemble of limited number of experts, which are diversified using a cooperation loss. However, this method is limited to task-aware settings. Doan al. (2022) diversifies the ensemble by training tasks different subspaces of models and then merging them. In contrast to our approach, the method requires exemplars to do so.
single network per
Gaussian Models in CL. Exemplar-free CIL methods based on cross-entropy classifiers suffer recency bias towards newly trained task (Wu et al.) 2019: Masana et al., (022). Therefore, some methods employ nearest mean classifiers with stored class centroids (Rebufhi et al. 2017 Yu et al.. 2020). SLDA (Hayes & Kanan 2020b) assigns labels to inputs based on the closest Gaussian, computed using the running class means and covariance matrix from the stream of tasks. In the context of continual unsupervised learning (Rao et al. 2019), Gaussian Mixture Models were used to describe new emerging classes during the CL session. Recently, (Yang et al., 202T), I), a fixed, pre-trained feature extractor and Gaussian distributions with diagonal covariance matrices were used to solve the CIL problem. However, we argue that such an approach has low plasticity and limited applicability. Therefore, propose improved method based multivariate Gaussian distributions and multiple experts that can learn new knowledge efficiently.
METHOD
The core idea of our approach is to directly diversify experts by training them on different tasks and combining their knowledge during the inference. Each expert contains two components: feature extractor that generates a unique latent space and a set of Gaussian distributions (one per class). The overlap of class distributions varies across different experts due disparities in expert embeddings. SEED takes advantage of this diversity, considering it both during training and inference.
Architecture. Our approach, presented in Fig. consists of K deep network experts gk f for l..... K, sharing the initial layers f for improving computational performance. f are frozen after the first task. We consider the number of shared layers hyperparameter (see Appendix A.3
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Moreover, each expert contains one Gaussian distribution G7: (Mir Σx) per class for its unique latent space.
During inference, perform ensemble of Bayes classifiers. The procedure is presented in Fig. 2
Algorithm.
Firstly, we generate representations of input x for each expert & as 73 = 9l f(z). Secondly, calculate log-likelihoods of rı. for all distributions G2 associated with this expert
(x)
In (|E + S (2T) + (rk (E() "rk pR),.
where S is the latent space dimension. Then, we softmax those values ".......
|cl
softmax ......... [[( per each expert, where the set of classes and 7 is a temperature. Class c with the highest average value after softmax over all experts (highest kx,55) is returned as predic- tion for task agnostic setup. For task aware inference, we limit this procedure to classes from the considered task.
Our training assumes T tasks, each corresponding to the non-overlapping set classes C1 UC U Cr C such that C n C. 0 for t Moreover, task is training step with only access to data D, = {(r, y) E C, ). and the objective is to train model performing well both for classes of task and classes of previously learned tasks (< t).
The main idea of training SEED, as presented in Fig. 3 is to choose and finetune one expert for each task, where the chosen expert should correspond latent space where distributions of new classes overlap the least. Intuitively, this strategy causes latent space to change as little as possible, improving stability.
Task
Task &
Task = distributions
Expert
gh
91
91
Expert
92
92
choose expert because distributions of new classes overlup least in its latent space.
finetune chosen expert further increase the separability of new clusses' distributions.
Task
3 (expert selection)
t (finetuning)
Figure 3: SEED training process for K 2 experts, T = 3 tasks, and |C,| 2 classes per task. When the third task appears with novel classes C3. we analyze distributions of C33 classes (here represented purple distributions) in latent spaces of all experts. We choose the expert where those distributions overlap least (here, expert 2). We finetune this expert to increase the separability of new classes further and move to the next task.
To formally describe our training, let us assume that we are in the moment training when we have access to data D, {xx, y)ly C.} of task for which we want finetune the model. There are two steps to take, selecting the optimal expert for task t and finetuning this expert.
Expert selection starts with determining the distribution for each class C, in each expert k. For this purpose, we pass all from D, with of vectors in latent space for which we approximate a multivariate Gaussian distribution Te.k- In consequence, each expert is associated with set Q2 = {q 92,ks ..., q/C, [. of [C,] distributions. We select expert & for which those distributions overlap least using symmetrized Kullback-Leible divergence dRL:
through deep network 9ı f. This results in a set
k
argmax &
Σ
dKL( gjk),
(2)
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To finetune the selected expert k we add the linear head to its deep network and train It using De set. As a loss function, we use cross-entropy combined with feature regularization based on knowledge distillation (Li & Hoiem 2016) weighted with L =
(1 =1-a))Lcr + aLKD, where 19% [(aa) 0k o [(:))] B is a batch and g% is frozen Fi-
Eie LKD [_|
While we use CE for its simplicity and effective clustering (Horiguchi et al. 2019), it can be replaced with other training objectives, such as self- supersuuervssoon Then, we remove the linear head, update distributions of Qi and move to the next task.
Due to the random expert initializations, we skip the selection procedure for K initial tasks and omit LKD. Instead, we select the expert with the same number as the number task (k = t) and use == Leg. For the same reason, we calculate distributions of new tasks only for the experts trained so far (k S t). Finally, we fix f after the first task so that finetuning one expert does not affect others.
4 EXPERIMENTS
In order to evaluate the performance of SEED and fairly compare with other models, we utilize three commonly used benchmark datasets in the field of Continual Learning (CL): CIFAR-100 (Krizhevsky 2009) (100 classes), ImageNet-Suue Subseet (Deng et al. 2009) (100 classes) and DomainNet (Peng et al. 2019) (345 classes, from 6 domains). DomainNet contains objects in very different domains, allowing us to measure models' adaptability to new data distributions. We create each task with a subset of classes from a single domain, so the domain changes between tasks (more extensive data drift). We always set K = 5 for SEED, so it consists of 5 experts. We evaluate all Continual Learning approaches in three different task distribution scenarios. We train all methods from scratch. Detailed information regarding experiments and the code are in the Appendix. We compare all methods with standard CIL evaluations using the classification accuracies after each task, and average incremental accuracy, which is the average of those accuracies (Rebuffi et al. 2017). We train all methods from scratch all scenarios.
The first scenario is the CIL. equal split setting, where each task has the same number of classes. This weakens the feature extractor trained on the first task, as there is little data. Therefore, this scenario better exposes the methods' plasticity. We reproduce results using FACIL(Masany et al. (2022), and PYCIL et al. 2021> benchmarks for this setting. We train all methods using random crops, horizontal flips, cutouts, and Aug (Hendrycks al. 2019) data augmentations.
The second scenario is similar to the one used in (Hou et al. 2019), where the first task is larger than the subsequent tasks. This equips CIL methods with a more robust feature extractor than the equal split scenario. Precisely, the first task consists of either 50% or 40% of all classes. This setting allows methods that freeze the feature extractor (low plasticity) to achieve good results. We take baseline results for this setting from (Petit et al.) 2023).
The third scenario is task incremental on equal split tasks (where the task id is known during inference). Here, the baseline results and numbers of models' parameters are taken from (Wang et al. 2022b).
We perform the same data augmentations as in this work.
4.1 RESULTS
Tab. I presents the comparison of SEED and state-of-the-ar exemplar-free CIL methods for CIFAR- 100, DomainNet, and ImageNet-Subset in the equal split scenario. We report average incremental accuracies for various split conditions and domain shift scenarios (DomainNet). We present joint training as an upper bound for the CL training.
SEED outperforms other methods by a large margin in each setting. For CIFAR-100, SEED is better than the second-best method by 14.7, 17.5, and 15.6 percentage points for = 10, 20, 50, respectively. The difference in results increases as there are more tasks in the setting. More precisely, for T' 10, SEED has 14.7 percentage points better accuracy than the second-best method (LwFF* which is LwF implementation with PyCIL (Zhou et al. 1021 data augmentations and learning rate schedule). At the same time, for T = 50 SEED is better by 15.6%. The results are consistent for other datasets, proving that SEED achieves state-of -fe-----thherrrr tht results in equal split scenario. Moreover, based on DomainNet results, we conclude that SEED is also better in scenarios with a significant distributional shift. Detailed results for CIFAR100 T=50 and NemainNet T=36 are presented in
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CIFARIOD T-50
DomainNet T-36
100
100
90
80
70
Accuracy
Accurscy
20
20
10
15
21
29
41
49
13 15
17
51
33
Finetune
EWC
LwF
FeTril
PASS
SSRE
122
Joint
SEED
Figure 4: Class incremental accuracy achieved after each task for equal splits on CIFAR 100 and DomainNet. SEED significantly outperforms other methods in equal split scenarios for many tasks (left) and more considerable data shifts (right).
Fig. + In this extreme setting, where each task consists of just little data, SEED results in significantly higher accuracies for the last tasks than other methods.
Table I: Task-agnostic avg. inc. accuracy (%) for equally split tasks on CIFAR-100, DomainNet and ImageNet-Subset, The best results are bold. SEED achieves superior results compared other methods and outperforms the second best method (FeTrIL)) by a large margin.
CIFAR. 100 Net
DomainNer
ImageNet-Suheet
CIL Method
7-50
T-36
7-10
Finetune
26.4+0.1
17.1+0.1
9.4+0.1
17.9+0.3
14.8+0.1
10.9+0.2
27.4=0.4
EWC Kirkpatrick et al 2017 (PNAS'17)
37.8:0.88
21.020.1
9.2:00.5
19.2:00.2
15.720.1
11.110.3
29.820.3
(CVP)177)
47.0=002
38.5=0.2
18.9-1.2
20.9:0.2
15.1=0.6
10.3+0.7
32.3=2044
PASS
Zh 2021
VPR221)
37,811.1
24.511.0
19.3-177
25.910..5
23.120..
9.8:0.3
IL2A
(NeurlP5'21)
43.5+0.3
28.3+1.7
16.4=09
20.7+0.5
18.2=0.4
16.2+0.4
SSRE
CVPR
44.2+0.6
215-188
33.2+0.7
24.0+110
22.1+0.7
45.0==055
ReTAI
Pett a1 0.. 2023
(WACV'23)
46.3-20.3
38.720.3
27.0-122
33.5::0.6
33.9220.5
27.540.7
58.7=0.2
SEED
61.7:10.4
56.210.3
426-1.4
45.0:-0.2
44.9-0.2
39.2+0.3
67.8-0.3
Joint
71.40.3
63.740.3
M9.104
69.10.11
81.5-03
Large first task class incremental scenarios. We present results for this setting in Tab. For CIFAR-100, SEED is better than the best method (FeTTrIL) by 4.6, 4.1, and 1.4 percentage points for T 6, 11, 21, respectively. For = 6 on ImageNet- than the best method. However, with more tasks, T' 11 T 21, FeTrIL with frozen feature extractor presents better average incremental accuracy.
SEED better by 3.3 percentage points
We notice that simple regularization-bant based methods such as EWC and LwF* are far behind more recent ones: FeTrIL, SSRE, and PASS, which achieve high levels of overall average incremental accuracy. However, these methods benefit from larger initial task, where robust feature extractor can be trained hefore incremental steps. In SEED, each expert can still specialize for different set of tasks and continually learn more diversified features even with using regularization like LwF. The difference between SEED and other methods is noticeably smaller in this scenario than in the equal split scenario. This fact proves that SEED works better in scenarios where a strong feature extractor must be trained from scratch or where there is a domain shift between tasks.
Task incremental with limited number of parameters. We investigate the performance of SEED in task incremental scenarios. We compare it against another state-of ofthh--art task incremental ensemble method- CoSCL (Wang et al. 2022b) and follow the proposed limited number of models' parameters setup. We compare SEED to: HAT (Serrà et al., 2018), MARK (Hurtado et al.) (021), and BNS (Qin cet al., 2021) Tab. presents the results with the number of utilized parameters. Our method requires significantly fewer parameters than other methods and achieves better average incremental accuracy, Despite being designed to solve the exemplar free CIL problem, SEED outperforms other task- incremental learning methods. Additionally, affects SEED's performance. Increasing the number of shared layers decreases required parameters but negatively impacts task-aware accuracy. As such, the number of shared layers in SEED is
check how the number of shared layers (f function)

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Table 2: Comparison of CIL methods on ResNet18 and CIFAR-I00 or ImageNet-Subset under larger first task conditions. We report task-agnostic avg. inc. accuracy from multiple runs. The best result in bold. The discrepancy results between SEED and other methods decreases compared the equal split scenario.
CIEAR- 100
ImageNer Subset
Method
T=21
[C]
C1|-50
[C]|-40
[C] -50
[C
[C||=40
EWC
Kirkpatrick et al.
2017 (PNAS117)
24.5
21.2
15.9
26.2
20.4
19.3
LwF* Meoulh 2017
(CVER112)
45.9
27.4
20.1
46.0
31.2
42.9
Pupessn
2018
18)
50.5
MUC
L1 ata
2020 (ECC
203
49.4
30.2
21.3
35,1
SDC
(EVPR'30)
58.9
ABD
Smidr o
202
(CCCV221)
57
PASS
7/m dirdral
A1ML
(CVPR'21)
63.5
61.8
58.1
64.4
61.8
51.3
112A
SSKE
2122
(CPPR223)
65.9
65.0
61.7
67.7
FeTil
Pelt
2023
(WACV'23)
66.3
65.2
61.5
72.3
71.2
67.1
Joint
M0.4
81.5
hyperparameter that allows for a trade-off between achieved results and the number of parameters required for training.
Table 3: Limited parameters setting on CIFAR-100 with random class order. The reported metric is average task aware accuracy (%). Results for SEED are presented for various numbers of shared layers. Although we designed SEED for the task agnostic setting, it achieves superior results to exemplar-free, architecture-baser methods using fewer parameters.
Table 4: Ablation study of SEED for CIL setting with T=10 on ResNet32 and CIFAR- 1100 Avg. inc. acc. is re- ported. Multiple components SEED were ablated. SEED as-designed presents the best performance.
Approach
Acc. (%)
Approach
#Params.
20-split
50-split
SEED(5 experts)
61.7 +0.4
HAT
6.8M
77.0
80.5
standard ensemble
56.9100.4
MARK
4.7M
78.3
weighted ensemble
57.0.10.5
BNS
6.7M
82.4
CoSCL ensemble
57.310.4
COSCL(EWC+LWF))
4.6M
79.4.1..0
87.911.1
multivariate Gauss.
53.5.10.5
SEED
3.2M
86.8
91.2.10.4
w/o covariance
54.1-10.3
SEED(1 shared)
3.2M
86.7100.6
91.2.1055
w/o temp. in softmax
59.2-10.5
SEED(11 shared)
3.1M
85.6110.3
89.6.10.2
SEED(21 shared)
2.7M
82.4:10.4
88.15
w/ ReLU
57.8++0.6
4.2 DISCUSSION
Is SEED better than other ensemble methods? We want to verify that the improved performance of our method comes from than just forming ensemble of classifiers. Hence, compare SEED with the vanilla ensemble approach to continual learning, where all experts are initialized with random weights, trained on the first task, and sequentially fine-tuned on incremental tasks. The final decision is obtained by averaging the predictions of ensemble members. We present results in Tab. Using the standard ensemble decreases the accuracy by 4.8%. We also experiment with the approaches where the predictions are weighted during inference by the confidence of the ensemble members (using prediction entropy, in (Ruan et al., (2023) and where the experts are trained with additional ensemble cooperation loss from (Wang et al.] 2022b). However, they yielded similar results to uniform weighting.
Diversity of experts Fig. aId Fig 11 (Appendix) depict the quality of each expert various tasks and their respective contributions to the ensemble. can be observed that experts specialize in tasks on which they were fine-tuned. For each task, there is always expert who exhibits over 2.5% points better accuracy than the average of all experts. This demonstrates that experts specialize in different tasks. Additionally, the ensemble consistently achieves higher accuracy (ranging from 6% 10% points) than the average of all experts on all tasks. Furthermore, the ensemble consistently outperforms the best individual expert, indicating that each expert contributes uniquely to the ensemble. See the
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details in Fig. 10 (Appendix) for more analysis of overlap strategy from Eq. that also presents how
experts are diversified between the tasks.
Expert
Relative Accuracy (%)
Expert
Expert
Expert
Expert 5
SEED
7
10
11
12
13 Task
14
15
16
17
18
19
20
Expert selected for finetuning
Figure 5: Diversity of experts on CIFAR- 100 dataset with T 20 split. The presented metric relative accuracy (%) calculated by subtracting the accuracy of each expert from the averaged accuracy of all experts. Black squares represent experts selected to be finetuned Although we do not impose any cost function associated with experts' diversity, they tend to specialize in different tasks by the design of our method. Moreover, our ensemble (bottom row) always performs better than the best expert, proving that each expert contributes uniquely to the ensemble SEED.
a given task.
Expert selection strategy. In order to demonstrate that our minimum overlap selection strategy (KL-max) improves the performance of the SEED architecture, we compare it to three other selection strategies. The first is random selection strategy, where each expert has equal probability of being chosen finetuning. The second a round-robin selection strategy, where for a task an expert with + (t 1 mod K) is chosen for finetuning. The third one is the maximum overlap strategy (KL-min), which we choose the expert for which the overlap between latent distributions of new classes is the highest. We conduct ten runs CIFAR-100 with Resnet32 architecture, three experts, and a random class order and report the average incremental accuracy in Fig. (6) Our minimum overlap selection strategy shows higher mean and median than the other methods.
Ablation study. In Tab. we present the ablation study for SEED. We report task-agnostic inc. avg. accuracy for five experts on CIFAR- 100 and ResNet32, where results are averaged over three runs. Firstly, we remove re- place particular SEED components. We start by replacing
T=20
T=50
34,00
the multivariate Gaussian distribution with its diagonal
Avg. Inc. Accuracy [%)
form. This reduces accuracy to 53.5%. Then, we re- move Gaussian distributions and represent each class as
Avg: Inc. Accuracy
$2,00
33,00
mean prototype in the latent space and use Nearest Mean Classifier (NMC) to make predictions. This also reduces accuracy, which shows that using multivariate distribution important for SEED accuracy. Secondly, we check the importance of using temperature in the softmax function during inference. SEED without temperature (r = 1) al-
30.00
28,00
28,00
41,00
27,00
25,00
achieves worse results than with temperature (r lowing more experts to contribute to the ensemble with more fuzzy likelihoods. At last, we analyze various SEED modifications, i.e., adding ReLU activations (like in the original ResNet) at the last layer, which decreased the accuracy by 3.9% points. It is because it is easier for the neural network trained with cross-entropy loss represent features Gaussian distribution if nonlinear activation is removed. See Tab, for additional ablation study.
Random
Robin
KL-min
KL-max
Figure 6: Avg. inc. accuracy of 10 runs with different class orderings for CIFAR- 100 and different fine-tuning expert se- Icction strategies for T 20, 50 and three experts. Our KL-max expert lection strategy yields better results than random, round-robin, and KL-min.
Plasticity vs stability trade-off. SEED uses feature distillation in trained expert to alleviate forgetting. To assess the influence of the regularization on overall method performance, we use the forgetting and intransigence measures defined in (Chaudhry et al. 2018). Fig. (left) shows the relationship between forgetting and intransigence for four different regularization- based methods: SEED, EWC as parameters regularization-base method, L.F regularization of network's output with distillation,
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100
[0.0, 1.0]
SEED (K=3) [0.9.
0.7
SEED (K=5) E [0.5, 0.999]
LwF X [1, 100]
0.6
[100, 10000]
Avg. line Accuracy (9)
Fethi
9.3
0.55
Finetune
0.4
Task Aware To
Task Agnostic To
0.1
Task Aware Ta 50
Task Ag Agnostic To 50
6.0
-0.1
0.0
0.1 Intransigence
0.2
0.3 0.4
30
Number of Experts
Figure 7: CIFAR-100. (Left) Forgetting and intransigence for different methods when manipulating the stability-plastictty parameters for T 10. SEED with experts achieves the best forgetting- intransigence trade-off. (Right) SEED accuracy function of number of experts for T = 20 with or 50 classes in the first task. Bars reflect standard dev. of three runs.
and recent FeTrIL method (Petit et al. 2023). For SEED, adapt plasticity using the a and K parameter, and for both EWC and LWF, we change the À parameter. FeTTrll method has no such parameter, as it uses frozen backbone. The trade-off between stability and plasticity evident. The FeTrIL model is very intransigent, with low plasticity and low forgetting. Plasticity is crucial in the CIL setting with or more tasks with an equal number of classes. Thus, EWC and LwF, need to be less rigid and exhibit more forgetting. The SEED model for K 3 and K = 5, achieves much better results than FeTrIL while remaining less intransigent and more stable than LwF and EWC. By adjusting the trade-off parameter of SEED, its stability can be controlled for any number of experts.
Number experts. Fig. 7 (right), we analyze how the number of experts influences the avg. incremental accuracy achieved by SEED. Changing the number of experts from to 5 increases aask-agnostic and task-aware accuracy by se 15% for Th = However, for To = 50, the increase is less significant ( (2% and 5% for task aware and task agnostic settings, respectively). These results suggest that scenarios with the significantly bigger first task are simpler than equal split ones. Moreover, going beyond five experts does not improve final CIL performance much.
CONCLUSIONS
In this paper, introduce exemplar-free CIL method called SEED. It consists of limited number of trained from scratch experts that all cooperate during inference, but in each task, only one is selected for finetuning. Firstly, this decreases forgetting, as only single expert model's parameters are updated without changing learned representations of the others. Secondly, it encourages diversified class representations between the experts. The selection based on the overlap distributions of classes encountered in a task. That allows us to find a trade-off between model plasticity and stability. Our experimental study shows that the SEED method achieves state-of-the-art performance across several exemplar- free class incrementllltt learning scenarios, including different task splits, significant shifts in data distribution between tasks, and tusk-incremental settings. In the ablation study, we proved that each SEED component is necessary to obtain the best results.
Reproducibility and limitations SEED We enclose the code in the supplementary material, and results can be reproduced by following the readme file. Our method has three limitations. Firstly, SEED may be not feasible for scenarios where tasks are completely unrelated and the number of parameters is limited, as in that case sharing initial parameters between experts may lead to a poor performance. Secondly, SEED requires the maximum number of experts given upfront, which can be found limitation of our method for settings. Thirdly, calculating distribution for class may not be possible if the class's covariance matrix is singular. We address the last problem by decreasing latent space size. We elaborate more this in the Appendix A.2

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BRGNOWLEDGNEENT
This research was supported by National Science Centre, Poland grant no 2020/39///SS6/0155L grant no 2022/5/B//ST602281,, grant 2022/47/B/SST6/033399 PL--Grid Infrastructure grant PLG/2022/016058 and the Excellence Initiative at the Jagiellonian University. This work was partially funded by the European Union under the Horizon Europe grant OMINO (grant number 101086321) and Horizon Europe Program
under the project "ELIAS: European Lighthouse of AI for Sustainability", GA no. 101 102277, it was also co-financed with funds from the Polish Ministry of Education and Science under the program entitled International Co-Financed Projects. Bartlomiej Twardowski acknowledges the grant KYC2021-03276533 Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor European Research Executive Agency can be held responsible for them.
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A APPENDICES
A.1 IMPLEMENTATION DETAILS
All experiments are the average over three runs and all methods are trained from scratch as in their original papers. We implemented SEED in FACIL (Masana et al., (002) framework using Python programming language and Py PyTorhh (Paszke et al. 2019) machine learning library. We utilized computer equipped with AMD EPYCIM 7742 CPU and NVIDIA A- A-100 GPU perform experiments. On this machine, SEED takes around hour to be trained on CIFAR100 for 7 = 10.
For all experiments. SEED trained using the Stochastic Gradient Descent (SGD) optimizer for 200 epochs per task, with momentum of 0.9, weight decay factor equal 0.0005, a set to 0.99, set to 3 and an initial learning rate of 0.05. The learning rate decreases ten times after 60, 120, and 160 epochs. As the knowledge distillation loss, we employ the L2 distance calculated for embeddings in the latent space. We set the default number of experts and class representation dimensionality S to 64. In order to find the best hyperparameters for SEED, we perform a manual hyperparameter search on a validation dataset.
Tab. 1 Fig. 4 Hand Fig. 19, We perform experiments for all methods using implementations provided in FACIL and PyCIL (Zhou et al. 202I) frameworks. We use ResNet32 as a feature extractor for CIFARIO0 100 and ResNet18 for DomainNet and ImuseNet-Subbet. For DomainNet T 12, use 25 classes per task; for T = 18, we use 10; for T 36, we use 5.
All methods were trained using the same data augmentations: random crops, horizontal flips, cutouts, and AugMix (Hendrycks et al., 2019). For baseline methods, we set default hyperparameters provided in benchmarks. However, for LWF, use À 10 observed that this significantly improved its performance.
Tab. 2] For baseline results, we provide results reported in (Petit et al. 2023 All CIL methods use the same data augmentations: random resized crops, horizontal flips, cutouts, and AugMix (Hendrycks) al. 2019).
Tab. The setting and baseline results are identical to (Wang et al. 2022b). We train SEED with the same data augmentation methods as other methods: horizontal flips and random crops. Here, five experts consisting of the Resnet32 network.
Fig. 6 We calculate relative accuracy by subtracting each expert's accuracy from the average accuracy of all experts as in (Wang et al., 20226). We perform 10 runs with random seeds.
Fig.F 7
Below we report the range of used parameters for plotting the forgetting-intraanigenne curves (Fig. 7 left).
LwF: X E [1, 2, 5, 7, 10, 15, 20, 25, 50, 100}
EWC: À E {100, 500, 1000, 2500, 5000, 7500, 10000}
SEED K = 1: E 00.0, 0.5, 0.9, 0.95, 0.97, 0.99, 0.999}
SEED K 3: 3: (0.9, 0.95, 0.96, 0.97, 0.98, 0.99, 0.999}
SEED K = 5: y (0.5, 0.9, 0.97,0.999)
A.2 RESNET ARCHITECTURE MODIFICATION
The backbone for the SEED method can be any popular neural network with its head removed. This study focuses on family of modified ResNet (He et al. 2016) architectures.
ResNet architecture a typical neural architecture used for the continual learning setting. In this work, we follow this standard. However, there are two minor changes to ResNet in our algorithm.
Due to ReLU activation functions placed at the end of each ResNet block, latent feature vectors of ResNet models consist of non-negative elements. That implies that every continuous random variable representing a class is defined on [0; oo) )$ where S is the size of a latent vector. However, Gaussian distributions are defined for random variables of real values, which, in our case, reduces the ability to represent classes as multivariate Gaussian distributions. In order to alleviate this problem, we remove the last ReLU activation function from every block in the last layer of ResNet architecture.
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Secondly, the size S of the latent sample representation should be adjustable. There for that. Firstly, if S is too big and number class samples is low, E can be a singular matrix. This implies that the likelihood function might not be well-defined. Secondly, adjusting S allows to reduce the number of parameters the algorithm requires. We overcome this issue by adding a 1x1 convolution layer with S kernels after the last block of the architecture. For example, this allows us to represent feature vectors of Resnet18 with 64 elements instead of 512.
A.3 MEMORY FOOTPRINT
SEED requires:
S(S +
00 + K|0,I+
ΣΣR 1,,(5 j=i
parameters run, where 10yl and 10gl represent number of parameters of and g functions, respec- tively. S is dimensionality of embedding space, K is number of experts, T is number of tasks, [Cy is number of classes in j-th task.
This total number of parameters used by SEED can be limited in several ways:
Decreasing S by adding a convolutional Ixl bottleneck layer at the network's end.
Pruning parameters.
Performing weight quantization.
Using simpler feature extractor.
Increasing number of shared layers (moving parameters from into f function).
Simplifying multivariate Gaussian distributions to diagonal covariance matrix or prototypes.
A.4 NUMBER OF PARAMETERS VS ACCURACY TRADE-OFF
To investigate the trade-off between the number of the SEED's parameters and the overall average incremental accuracy of the method, we conducted several experiments with different number of experts and shared layers (as in a previous experiment in Tab. 3 we only adjust the number layers). We see that these two factors indeed control and decrease the number of required parameters, e.g., sharing the first 25 layers in Resnet32 decreases memory footprint by 0.8 million parameters when five experts. However, it slightly hurts the performance of SEED, the overall average incremental accuracy drops by 4.4%. These results, combined with expected forgetting/inraassiennee guide application of SEED for particular problem.
We additionally compare SEED to the best competitor FeTrIL (with Resnet32) low parameters regime. FeTrIL stores feature extractor without the linear head and prototypes of each class. For SEED we utilize Resnet20 network with number of kernels changed from 64 to 48 in the third block. We use 5 experts which share first 17 layers. We present results in Tab. We present the number of network weights and total number of parameters which for SEED also includes multivariate Gaussian distributions. SEED has 13K less parameters than FeTrIL but achieves better accuracy settings.
Table 5: SEED outperforms competitors in terms of performance and number of parameters cqually split CIFARIO0 however requires decreasing size of the feature extractor network and sharing first 17 layers.
CIFAR-100
Network weights
params.
T=10
T=20
7=50
EWC-
Kirkpstrick et 2017 (PNAS'17)
ResNer32
473K
24.5
21.2
LwF
[kenotii el ar 2017 (C (PR1)7
Resnet32
473K
473K
45.9
27.4
20.1
FeTrIL.
Peirer 2025
(WACV
Resnet32
473K
473K
46.3-1033
38.7--0.3
27.0+11.2
SEED
Resne120
339K
460K
$4.7=0.2
48.620.3
33.1+11.1
A.5 PRETRAINING SEED
We study the impact of using pretrained network with SEED on its performance. For this purpose utilize ResNet- with weights pretrained ImageNet-1K 1 as feature extractor for every expert.
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3.0
Number of experts
646
96.6
62.3
(1.
682
62.5
Avg. Inc. Acquraa (%)
Number of experts
XTAM
11.00
1.90
1.000
2:14
Number of parameters
2.5
141
00.1
144
60.0
2:00W
2.33
ResNet32
57.5
2.0
55.0
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2
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1.0
46.
433
1:
7.7M
50.0
0
1
11
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25
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25
Number of shared layers
Number shared layers
Number of experts
S0.b
Sa.8
Mi2
944
4722
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Avg. Inc. www.Aoouuraaa (%)
Number experts
5412M
50
Number of parameters
Aocurad Marr
52.5
543
548
525
4.
IC.EM IEA CMN 43 37.177 20-317
ResNet18
50.0
NATOM 36.1M 8MM
30
47.5
2
411
111
611
411
45.0
15 Number of shared layers
1 5
0
9
13 15
Number of shared layers
Figure 8: Impact of number of experts and number of shared layers accuracy and number
parameters of SEED. We utilize CIFAR100 with |T] 10. We can observe that accuracy drops when decreasing the number of experts and increasing the number of shared layers.
DomainNet
Avg. Inc. Accuracy (%) Pretained
Forgetting (%)
[7]
From scratch
From scratch Pretrained
12
45.0
53.1
12.1
12.8
24
44.9
54.2
11.2
12.1
36
39.2
53.6
13.7
15.6
Table Pretraining experts in SEED increases its accuracy while slightly increasing forgetting. We compare ResNet-18 with weights pretrained ImageNet-I to randomly initilized ResNet-18 as feature extractors of each expert.
We compare it to SEED initialized with random weights (training from scratch) in Tab Pretrained SEED achieves better average incremental accuracy by: 8. 8.1%. 9.3%, 14.4% on DomainNet split to 12, 24, tasks respectively.
A.6 ADDITIONAL RESULTS
In this section provide more experimental results. Fig. presents insight into diversity of experts on C1FAR100 for T' = 50 and 5 experts. We measure value of overlap per expert in each task given by Eq. Average value of the function differ between tasks, e.g., for task 49, equals to 3.5, while for task 33. it equals to ze 18.0. This is due to semantic similarity between classes in a given task, classes in task 49. (cups, bowls) are semantically more similar then classes task 33. (bed, dolphin). However, in each task we can observe significant variance between values for experts. This proves that classes overlap differently experts, therefore experts are diversified what allows SEED to achieve great results.
In Fig. 9 we present accuracies obtained after equal split tasks Table We report results for CIFARTO0 and DomainNet datasets. We observe that for DomainNet SEED achieves 15% better accuracy after the last incremental step than FeTril. On CIFAR100 SEED achieves 20% and 16% better accuracy than the best method. This proves that SEED achieves superior results to state-of-the-ant methods on equal-split and big domain shift settings.
Table 7 presents additional ablation study for SEED. We test various ways to approximate class distributions in the latent space on CIFAR100 dataset and = 10. Firstly, we replace multivariate Gaussian distribution with Gaussian Mixture Model (GMM) consisting of 2 multivariate Gaussian
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DomainNet 1=12
Net
90
80
Aue. Accuracy
70
Noss Accursy/
70
60
50
40
30
20
10
10
CIFAR10O T=10
CIFAR100 T-20
A0g ACTUSSY
90
10
10
30
5
10
52
13
14
15
18
19
netune
EWC
Fe m1
SSRE
12A
Joint
SEED
Figure 9: Accuracy after each task for equal splits on CIFARI00 and DomainNet. SEED significantly outperforms other methods in equal split scenarios for many tasks (top) and more considerable data shifts (bottom).
distributions. slightly reduces the accuracy (by 1.2%). Then, we abandon multivariate Gaussians and approximate classes using 2 and Gaussian distributions with the diagonal covariance matrix. That decreases accuracy by a large margin. We also approximate classes using their prototypes (centroids) in the latent space. This also reduces the performance of SEED.
Table 7: Ablation study of SEED for CIL setting with T=10 ResNet32 and CIFAR-100. Avg. inc. accuracy is reported. We test different variations class representation (such as Gaussian Mixture Model, diagonal covariance matrix prototypes). SEED presents the best performance when used as designed.
Approach
Acc.(%)
SEED(5 experts)
61.7 =0.5
w/ 2x multivariate
60.5+0.7
2x diagonal
53.8 10.1
w/ 3x diagonal
53.8+L0.3
prototypes
54.1++0.3
16

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Published as a conference paper at ICLR 2024
5:45
7.13
Task:
5.47
5.68
Task:
4.91
412
3.38
5.54
Task: 10
18.20
25.50
11:33
44.92
45.03
18
Task: 11
16.17
24.20
10.41
23.09
10.04
[ask: 12
3.51
4.89
4.31
4.80
4.17
Task: 13
16839
11.52
51.27
8.73
16.87
lask: 14
5.84
5.04
4.07
4.48
3.99
Task: 15
b.
6.64
4.91
7.36
5.05
16
Task: 16
11.19
1841
10.01
19.08
31.98
Task: 17
9.32
12.38
7.36
8.21
19.48
Task: 18
10:73
10.56
5.60
6.23
6.05
lask: 19
19:1
17.09
12.99
20.22
23.93
Task: 20
7.
13.98
10.27
10:39
7:20
Overlap value (Eq. 2)
lask:
1,17
Task: 22
11:92
Task:
Task:
Task: 25
5.09
6.12
5.02
6.11
11.36
12
Task: 26
12:65
10:41
7.68
10:15
7,90
Task: 27
12:98
25:12
7.76
10.02
16.44
Task: 28
14.41
13:55
13.87
4.68
12101
lask: 29
lask: 30
15:23
2.53
20.64
3,00
9.94
3.59
29.82
2.64
50.18
2,85
10
lask: 31
11.93
24 26
7.50
12.07
5.84
Task: 32
7.30
10131
5,95
5.99
3.04
lask: 33
12:15
18.72
21.80
13.01
24.40
lask: 34
12.10
20.30
17.61
11.21
13:73
lask: 35
5,82
V.30
4152
4.23
4.24
Task: 36
10:23
10.09
8.70
8:03
Task: 37
lask: 38
9.12
Task:
8.68
[ask:
95
5.31
6.29
8.25
16
[ask:
10.24
[ask:
13:83
14:07
8.60
6.65
11.68
Task: 43
7.58
10.12
11.80
10:48
7.24
Task: 44
11 88
10.49
14.05
08.48
11:09
lask: 45
7.98
12.82
13.99
8.46
8.44
-4
lask: 46
5.26
5.09
4.13
5.16
6.17
Task: 47
11:43
9.00
10:00
10:31
7.06
lask: 48
8.46
8.27
12.83
6.95
8.96
Task: 49
3.04
3.29
3.34
3.48
4.19
Expert
Expert
Expert
Expert 4
Expert 5
Figure 10: Overlap of class distributions in each task per expert on CIFAR-100 100 dataset with T = 50 split and random class order. Diversification by data yields high variation overlap values in each task what proves that experts are diversified and learn different features. Average overlap values differ between tasks, as they depend on semantic similarity of classes. Classes task 49. are semantically similar (cups, bowls) but classes in task 33. are different (beds, dolphins).
17

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Published as a conference paper at ICLR 2024
Expert 1
Relative Accuracy (%)
Expert
Expert 3
Expert
Expert 5
SEED
88
10
12
14
16
18
20
Task
Experts selected for finetuning
8
Expert
Relative Accuracy (%)
Expert 2
4
Expert
Expert 4
Expert
SEED
88
6
8
10
11
12
13 Task
14
15
16
17
18
19
20
Experts selected for finetuning
Figure 11: Diversity experts on CIFAR-100 dataset with split, different seeds and random class order. The presented metric is relative accuracy (%). Black squares represent experts selected to be finetuned on a given task. We can observe that experts specialize different tasks.
18


Reviews:
[{"review_id": "pU5n1hZXLi", "reviewer": "Reviewer_Hjf8", "summary": "The paper proposes an (exemplar-free) ensemble method for Class Incremental Continual Learning (CIL). A fixed number of experts are trained on a stream of tasks. At each task, only one expert is finetuned. The result is a diverse expert ensemble which is reported to perform very well on several benchmarks.", "questions": "1. In Page 8, it's written that *\"SEED uses a regularization method known from LwF\"*. Are you actually using LwF for each expert? I don't recall reading it in other places in the paper. Please clarify.\n1. How are the hyperparameters of EWC and LwF set in the experiments ? For instance, Figure 7 shows $\\lambda\\in [100,10000]$ without any justification.\n1. In Page 1, the authors mention *\"The trend is evident... results steadily improve over time\"*. What time?\n1. In Table 1, how many repetitions are performed per experiment?\n1. In Table 1, why aren't there results for ImageNet-Subset for two algorithms?\n1. In Figure 5, the presented metric is \"relative accuracy\". Relative to what?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "I believe this paper advances the field of continual learning.\n\nSpecifically,\n\n- Tackles a popular continual learning scenario.\n- Good empirical results. Show significant improvements on several datasets.\n- Well written and clear.\n- Sound experiments with several ablation studies.", "weaknesses": "No major weaknesses. Perhaps just the fact that several models (experts) have to be trained and stored (but this is inherent in ensemble methods).\n\n\n### Minor remarks (I don't expect any response on these remarks in the rebuttal) \n- In Figure 3, it's hard to see that task 3 overlap least with the second expert. Perhaps make this a bit clearer.\n- In Page 4, perhaps the inference and training algorithms would be easier to read in an \"Algorithm\" structure rather than simple paragraphs.\n- In Page 5, just before Section 4.1., the task incremental scenario is mentioned but is not really defined properly in my opinion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "ayUa90RD0O", "reviewer": "Reviewer_3XPS", "summary": "The authors propose a new class-incremental learning algorithm which relies on a set of expert models with a shared backbone. In each training step, only one expert is trained on the new task. This expert is chosen based on how well it separates the new classes. At inference time, all experts make predictions but their contribution to the final prediction is conditioned on the input.\nExperiments are conducted on three different datasets in two variants and for task-agnostic and task-aware cases.\nThe authors provide several ablation studies on core components of the method.", "questions": "Figure 7 (right): what is the difference between SEED with 1 expert and finetuning? What is your explanation that your method is doing very well in this particular setup?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The authors make an interesting observation by showing that some methods work significantly better if more data is available initially.\nThey propose a method that improves over the baselines, in particular in the case where few data is available initially. The method and description is good and reminds me of S-Prompts which has a similar setup but works with pretrained transformer models.\nAs far as I can tell, all important aspects are covered by ablation studies, leaving only room for few questions.", "weaknesses": "A not fully covered discussion is the number of parameters vs accuracy tradeoff. The proposed methods requires significantly more parameters than some of the baselines which might be a limitation if the models are extremely large. Furthermore, training from scratch is a rather uncommon scenario. It is unclear how this method would work if a pretrained model is used.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "2jBSD3iOAY", "reviewer": "Reviewer_hfuR", "summary": "This work studies the class incremental learning (CIL) problem from the mixture-of-experts (MOE) angle where only a subset of the model (expert) is activated for each task. To this end, the authors propose SEED that selects one expert per task during continual training and aggregates the experts during evaluation. Thus, SEED promotes diversity representations while requiring no task identifiers during inference. Experiments on various CIL benchmarks show promising results of SEED.", "questions": "- Performance comparison under a fair setting.\n- How could SEED avoid forgetting when the number of tasks is significantly larger than the number of experts.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- This work studies the MoE for CIL, which has been gaining interests as a promising approach to CIL.\n- The proposed method is simple yet demonstrated encouraging results.\n- The authors conduct various ablation studies to explore different aspects of SEED.", "weaknesses": "## Major concern - experiment\n- In table 1 and 2, although it is clear that SEED performs better than the baselines, different methods have different memory footprint (model parameters and other components) or training complexities. Thus, it is difficult to judge if the gains come from the proposed method or from the additional complexities.\n\n## Major concern - conceptual drawback of SEED\n- SEED implies that the number of experts should be smaller than the number of tasks and there is only one expert activated per task. Thus, it is inevitably that some experts will be reused in the future, leading to catastrophic forgetting especially when the tasks are conflicting. The use of LwF regularization might not be helpful if the expert stay inactive for a long period. \nTogether with with the ambiguity in the experimental settings and results, the contribution of this work seems marginal.\n\n## Minor concern \n- Additional baselines: A recent related method [A] that seems to outperforms SEED should be discussed.\n- There are two different references for CoSCL in the Introduction.\n\n[A] Ardywibowo, Randy, et al. \"VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty.\" International Conference on Machine Learning. PMLR, 2022.\n\n\n## After Rebuttal\nAfter the rebuttal, the authors addressed most of my concerns and I adjust the rating accordingly. In the final version, the authors are strongly encouraged to clearly state the assumptions and scenarios that the proposed method will work well on.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "y5Ffd3PjEC", "reviewer": "Reviewer_rq4G", "summary": "This paper presents Selection of Experts for Ensemble Diversification (SEED), an algorithm that tackles exemplar-free class-incremental learning (CIL). Existing exemplar-free CIL algorithms incrementally trains new local experts on new tasks, and SEED provides a task selection technique to diversify trained experts, mitigate forgetting, and maintain plasticity. Specifically, an expert is a Bayes classifier that targets a distinct set of classes, with each class corresponding to a Gaussian distribution. To diversify expert selection, the next task is selected via the highest KL-divergence from encountered distributions. Inference is done by averaging the logits output from each expert and selecting the class with highest probability.", "questions": "Please address the two weakness points above.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper clearly identifies a problem of task diversification in CIL.\n2. The language is easy to follow.", "weaknesses": "1. Lack of novelty: selecting the next task based on largest KL divergence in continual learning is not a novel strategy. So is the expandable MoE architecture and the inference method.\n2. Overhead growth: one biggest disadvantage of architecture expansion is that the memory overhead grows linearly with respect to the number of tasks. That is, it needs K experts for K tasks with nothing reusable. SEED omits the selection procedure to decide the number of experts needed even if the number of tasks is large. This is not scalable in continual learning, where the number of tasks can be unbounded.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)