Saliency-driven Experience Replay for Continual Learning
Authors: Giovanni Bellitto
Federica Proietto Salanitri
Matteo Pennisi
Matteo Boschini
Lorenzo Bonicelli
Angelo Porrello
Simone Calderara
Simone Palazzo
Concetto Spampinato
Venue: NeurIPS 2024 spotlight (2024)
Keywords: Continual Learning
Transfer Learning
Saliency Prediction
Primary area: online_learning

Abstract: We present Saliency-driven Experience Replay - SER - a biologically-plausible approach based on replicating human visual saliency to enhance classification models in continual learning settings. Inspired by neurophysiological evidence that the primary visual cortex does not contribute to object manifold untangling for categorization and that primordial saliency biases are still embedded in the modern brain, we propose to employ auxiliary saliency prediction features as a modulation signal to drive and stabilize the learning of a sequence of non-i.i.d. classification tasks. Experimental results confirm that SER effectively enhances the performance (in some cases up to about twenty percent points) of state-of-the-art continual learning methods, both in class-incremental and task-incremental settings. Moreover, we show that saliency-based modulation successfully encourages the learning of features that are more robust to the presence of spurious features and to adversarial attacks than baseline methods. Code is available at: https://github.com/perceivelab/SER

matteo. matteo.pennisii @phdd. unict. it Saliency-driven Experience Replay for Continual Learning
Giovanni Bellitto
Federica Proietto Salanitri
University of Catania
University of Catania
giovanni wew.iitoouuiitt..t.....
Matteo Pennisi
Matteo Boschini
University of Catania
University of Modena and Reggio Emilia matteo.
Lorenzo Bonicelli
Angelo Porrello
University of Modena and Reggio Emilia lorenzo. bon i ce 1 Cunimores it
University of Modena and Reggio Emilia angelo. poreel.poorre lo@unimores it
Simone Calderara
Simone Palazzo University of Catania
University of Modena and Reggio Emilia s c waweeeaaeeeerrreemmorreee
s pamnonepallazzouuiicce
Concetto Spampinato
University of Catania
woncetto.spampinatccceee.
Abstract
We present Saliency-driven Experience Replay- SER biologically-plausiin approach based replicating human visual saliency to enhance classification models in continual learning settings. Inspired by neurophysiological evidence that the primary visual cortex does not contribute to object manifold untangling for categorization and that primordial saliency biases are still embedded the modern brain, we propose to employ auxiliary saliency prediction features as a modulation signal to drive and stabilize the learning of a sequence of non-i.i.d. classification tasks. Experimental results confirm that SER effectively enhances the performance (in some cases up to about twenty percent points) state-of-the-art continual learning methods, both in class-incremental and task-incremental settings. Moreover, we show that saliency-base baased modulation successfully encourages the learning of features that are more robust the presence of spurious features and to adversarial attacks than baseline methods. Code is available at: https: //githut com/ peerceiveaabSE..
Introduction
Humans possess the remarkable capability keep learning. with limited forgetting of past experience. and to quickly re-adapt to new tasks and problems without disrupting consolidated knowledge. Machine learning, on the contrary, has shown significant limitations when dealing with non-stationary data streams with a limited possibility to replay past examples. The main reason for this shortcoming
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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can be found in the inherent structure, organization and optimization approaches of artificial neural networks, which differ significantly from how humans learn and how their neural connectivity is built when accumulating knowledge over a lifetime. According to the Complementary Learning Systems (CI.S) theory [46, 33], the human ability to learn effectively may be due to the interplay between two learning processes that originate, respectively, on the hippocampus and on the neocortex. This theory has inspired several continual learning methods [29, 40, 28]. In particular, the recent DualNet method [51] translates CLS concepts into a computational framework for continual learning. Specifically, it employs two learning networks: a slow learner, emulating the memory consolidation process happening in the hippocampus through contrastive learning techniques, and fast leamer, that aims at adapting current representations to new observations. However, this strategy still appears insufficient for addressing the problem of continual learning. because it starts from the (possibly wrong) assumption that human neural networks directly process visual input with the objective of performing categorization from early vision layers. On the contrary, neurophysiological studies [19, 32] are in near universal agreement that the object manifolds conveyed to primary visual cortex VI (one of the earliest areas involved in vision) words, the neurons of the earliest vision areas do not contribute to object manifold untangling for categorization, but rather enforce luminance and contrast robustness [32]. This suggests that training early neurons with a visual categorization objective - as done not only in DualNet, but in all existing continual learning methods Moreover, recent studies on the causes of forgetting in artificial neural networks showed that deeper layers (i.e., closer to the output) are less stable in presence of task shifts [53], which is consistent with the hypothesis that earlier layers do not bear specific categorization responsibilities.
tangled as the pixel space. In other
is in stark contrast to the biological counterparts observed in primates.
Given these premises, is peculiar that existing bio-inspired continual learning methods tend ignore all upstream neural processes underlying visual categorization, such as visual saliency processes. Indeed, the ability select relevant visual information appears to be the hallmark of human/primate cognition. Moreover, recent findings in cognitive neuroscience have shown that the visual attention priorities of human hunter-gatherer ancestors are still embedded in the modern brain [48]: humans pay attention faster to animals than to vehicles, although we now see more vehicles than animals. This primordial saliency bias embedded in human brains suggests that the neuronal circuits of the ventral visual pathway are somehow inherited, as form of genetic legacy from ancestral experience, and tend to remain stable over time- thus not subject forgetting, though we have long stopped hunting to survive. Interestingly, we observed the same forgetting-fres behavior for saliency prediction on artificial neural networks. Fig. shows the trend of the similarity [10] metric for a saliency prediction model trained in a continual learning scenario, and compares it to the accuracy of classification model under the same settings. While classification accuracy drops as the classifier learns new classes, the saliency metric remains stable, and even slightly improves.
Clase-L. Accurssy
Sinkarty
0.55
0.84
0.03
0.22
0.50
75
TI
T5 T10
T15
T20
Figure 1: Comparison of Forgetting-Free Saliency Prediction vs. Catastrophic Forgetting in Classifiers and Activation Maps in Continual Learning Scenarios. (Left figure): The saliency accuracy (measured by the similarity [10] score) of a saliency predictor trained a continual learning setting improves as more tasks introduced, while the classification accuracy of continual classifier degrades over time, indicating that saliency detection remains i.i.d. even with non-i.i.d. data. (Right figure): The top row shows activation maximization maps via GradCAM, which are prone to catastrophic forgetting due to their dependence on the classifier. In contrast, the bottom row shows saliency maps produced by the predictor, which remain stable and consistent over time.
From this observation, in this paper we propose SER, a Saliency-driven Experience Replay strategy that employs visual saliency prediction [6] to drive the learning of sequence of classification tasks in a continual learning setting. To emulate what has been observed in primates, where visual saliency modulates the firing rate of neurons that represent the attended stimulus at different stages of
2

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visual processing [63, 45], SER adopts a two-branch model: one branch performs visual salieney prediction [37, 27, 20], and its responses modulate the features learned by paired classification model in the second branch.
While the SER strategy stands out its approach, it's important to note a similar category of methodologies that utilize attribution maps (e.g., computed via GradCAM), also known as attention
maps, as a distilled form of classifier knowledge for future replay [61, 18, 22, 59, 3]. However, saliency prediction maps are significantly different from attribution maps. Indeed, attribution maps elucidate the inner workings of DNNs by highlighting relevant input features for predictions and such they suffer catastrophic forgetting (as shown in Fig. 1), while saliency maps, rooted in neuroscience and human visual processing, aim to emulate how humans perceive and prioritize visual information, and, most importanly, they are forgetting-free free.
SER is model-agnostic and can be used combination to any continual learning method. We demonstrate that saliency modulation positively impacts classification performance in online continual learning settings, leading significant gain accuracy (up 20 percent points) w.r.t. baseline methods. We further demonstrate the usefulness of saliency modulation different benchmarks (including a challenging one that tackles fine-grained classification) and substantiate our claims through set of ablation studies. We finally show that saliency modulation, besides being biologically plausible, leads to learn saliency-modulated features that are more robust to the presence spurious features and to adversarial attacks.
Seliency
Gradient
Saliency Prediction
Prediction
Ground Truth
SER
SEA
SER
SER
SER
Attention Modulation
Grodieni
Classitor Enooder
Prodictor
Classification
Figure 2: Architecture of the proposed Saliency-driven Experience Replay (SER) strategy. The classification backhone is paired with saliency prediction network that, given its capability of being forgetting-free, aims adjusting the learned classification features order mitigate overall forgetting.
2 Related Work
Continual Learning (CL) [47, 16, 49] addresses the problem of catastrophic forgetting in neural networks, wherein they tend to lose previously acquired knowledge when faced with shifts in input data distribution. Various solutions have been proposed to address this, including the incorporation of regularization terms [31. 74], specific architectural designs [60, 44], and rehearsal of previously encountered data points [57, 55, 9]. However, the application of these solutions to real-world scenarios is challenging due evaluations often being based on unrealistic benchmarks [1, 65]. Online Continual Learning (OCL) [43] addresses this challenge by limiting multiple epochs on the input stream, reflecting the realistic assumption that data points encountered in weal-world settings occur only once. To address this challenge, many strategies adopt replay approach [54, 57]. Some focus on memory management: GSS [2] optimizes the basic rehearsal formula to store maximally informative samples, while HAL [14] identifies synthetic replay data points maximally affected by forgetting. CoPE [15] employs class prototypes for gradual evolution the shared latent space, while ER-ACE [1 Jadjusts the cross-entropy loss asymmetrically to minimize task imbalance. Our proposal adopts a remarkably different approach w.r.t. these classes of methods, in that we take inspiration from cognitive neuroscience theory of learning and exploiting the features of conjugate forgetting-fire free task (i.e., saliency prediction) to modulate the responses of our OCL model. Doing so produces stabilizing effect on model and makes it more resilient forgetting.
An approach similar the spirit to ours [39] that leverages saliency prediction for exemplar-fres class incremental learning. To compensate for the absence of past task data, this methods relies

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pre-trained saliency detector, which remains frozen throughout the learning process, providing guidance for attribution maps of the classification backbone. Consequently, it tackles the challenge of forgetting by employing a pre-trained backbone to constrain feature drift. In contrast, SER operates on dynamic framework where the visual saliency network is continuously trained, showcasing remarkable resistance to forgetting, while concurrently modulating the drift of classification features. This approach offers a more flexible visual saliency-classificate
paradigm that adapts to any dataset without external dependencies, as opposed to [39], which requires the use of pre-trained saliency detector trained on the same data distribution as the target data.
Another approach, similarly inspired by cognitive theories, is DualNet [51], which employs two networks that loosely emulate how slow and fast learning work in humans. However, DualNet employs contrastive learning on the slow network (the earliest layers of the model), while it seems that abject-identiffing transformations happens later in the human visual system [19, 32]. Our results, reported later, substantiate the suitability of our choice to use low-level processes, such as saliency prediction, to drive continual learning tasks, rather than contrastive learning or classification pre-training techniques as, respectively, in DualNet and TwF [8].
Though the concept of utilizing saliency prediction maps in online continual learning is relatively new, recent trends have shown promising advancements in mitigating forgetting by encouraging models to recall evidence for past decisions, stored as activation maps [22]. Specifically, [22, 59, 3] employ attribution methods, such as Gradient- to compute and store visual model explanations for each sample (or parts thereof) in the buffer and ensures model consistency with previous decisions during the training phase. Similarly, Dhar [18] adopts Grad- CAMM but does not store any information, it employs knowledge distillation on the activation maps across consecutive tasks. However, as presented in the introduction, there is a fundamental distinction between saliency maps and activation maps with the latter being subject to forgetting, while the former not (Fig. 1).
Class Activation Mapping (GradCCAM, [61].
Finally, our approach diverges from the recent trend in the continual learning (CL) field, which primarily employs foundation models (mostly Vision Transformers, ViTs) and focuses on learning prompts to mitigate forgetting [68, 67, 24, 62). The main limitation of these methods is that they are restricted to transformer based architectures. In contrast, our strategy does not rely on any specific model type, thereby enhancing potential impact on real-world applications.
Method
3.1 Online Continual Learning
Following the recent literature, we pose OCL as a supervised image classification problem with an online non-i.i.d. stream of data, where each training sample is only seen once. Although our saliency-driver modulation does not require the presence or knowledge of task boundaries, in this formulation and in our experiments we assume that these are given, the benefit of any baseline method enhanced by the proposed extension. More formally, let D = .Di,..., Dr} } be a sequence of data streams, where each pair (x, v) ~ D; denotes data point x E X with the corresponding class label E )) the sample distributions (in terms of both the data point and the class label) differ between separate streams D, and D,- - the sets of class labels in each stream are disjoint, though both belong to the same domain y. Given a classifier f X + Yr parameterized by 0, the objective of OCL is to train of on D. organized as a sequence of T' tasks (TI..... TT); under the constraint that, at generic task Ti the model receives inputs sampled from the corresponding data distribution, i.e.. (x, y) ~ D,, and sees each sample only once during the whole training procedure. The classification model may optionally keep a limited memory buffer M of past samples, to reduce forgetting of features from previous tasks. The model update step between tasks can be summarized as:
(f, 0,0,1,, D,-1, M.-1) + (f.
(1)
where 0 and M, represent the set of model parameters and the buffer at the end of task tú. respectively. For methods that do not exploit buffer, M, Ø, Vi
The training objective is to optimize a classification loss over the sequence of tasks (without losing accuracy on past tasks) by the model instance at the end of training:

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T
arg min Or
(xx) wD.
(2)
where L is a generic classification loss (e.g., cross-entropy), which a continual learning model attempts to optimize while accounting for model plasticity (the capability to learn current task data) and stability (the capability to retain knowledge of previous tasks) [47].
3.2
SER: Saliency-driven Experience Replay
Our method grounded on the neurophysiological evidence that iiiention-driven neuronal firing rate modulation is multiplicative and the scaling of neuronal responses depends on the similarity between a neuron's 's preferred stimulus and the attended feature [63, 45). This hypothesis translated into general artificial neural architecture, where we emulate the process of human selective attention through a visual saliency prediction network [6] whose activations modulate, through multiplication, neuron activations of paired classification network at different stages of visual processing. Formally, let S X' - S be a saliency prediction network, where X' is the space of input images and the space of output saliency maps. Generally, if X' 3x for RGB images, then S = R''ww where each location of a map E S measures the saliency of the corresponding pixel in the RGB space. We assume that S can be decomposed into functions,
encoder E X - H and decoder D : H - S, such that S (x) D (E (x)). for x E X. Then, given an online continual learning problem with data stream D and set of classes y, let C X + be a classification network, such that C' and the saliency encoder E share the same architecture (with independent parameters). An illustration of the proposed architecture is shown in Fig. 2.
At training time, both and C' observe the same data stream, from which pairs (x, y) of input data and class label are iteratively sampled. Through the use of external saliency oracle, we extend each data sample to a triple (x, y, s), where s is the target saliency map associated to x. The oracle of ground-truth maps, when available, or pseudo-labels provided as the output of
be either pre- pre-trained saliency predictor (unrelated to 5). We therefore proceed to optimize a multi-objective loss function L Lg + XCe, with À being weighing hyperparameter. Loss term L, is computed on the output of saliency predictor S. and compares the estimated saliency map S(x) with the target s by means of the Kullback-Leibler divergence (commonly employed saliency prediction objective [10, 20, 4, 69, 26]):
C. = Σ 8; log 5i(x) +
St
+e
with 8; and (x) iterating over map pixels in s and S(x), respectively. Loss term La encodes a generic online continual learning objective, as introduced in Eq. 2. As the proposed approach is method-agnostic, details on the formulation of f. may vary.
In order to enforce selective attention-driven modulation of classification neuronal activations, we leverage the architectural identity of saliency prediction encoder E and classifier C to alter the feedforward pass of the latter, by multiplying pre-activation features in C' by the corresponding features in E, before applying non-linearity and feeding them to the next layer of the network. Formally, let us assume that the C' and E networks consist of a sequence of layers (1..2,....441) Without loss of generality, let each layer compute its output as z; = a (W,2-1),, with a being an activation function, W, the network-specific layer parameters (i.e., not shared between E and C') and 2 1 the output of the previous layer (or the network's input x, appropriate). Then, let us distinguish between features (4) and z(1)) respectively representing the output of layer I, by the saliency prediction encoder S and the classifier C. We apply saliency-driven modulation by
(c) modifying the computation of z as follows:
(c) _ o (w.o)
(0 (2. (2)02221))
(4)
where @ denotes the Hadamard product. Intuitively, the proposed approach encourages the classifica- tion model to attend to "salient" features of the input, where the concept of saliency is generalized from the pixel space to hidden representations. descent optimization of L. would also affect on the saliency encoder E. This is undesirable,
important to note that, at training time, gradient
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previously showed (see Fig. 1) that saliency features are robust to task shifts, unlike classification features: hence, order guarantee this property. we stop the gradient flow from C, to parameters in E, and use it to update the parameters of classifier C' only.
In the above formulation, we assumed the presence of a classification network with fully-connected layers; however, our method can be applied in an agnostic manner to any method employing, at least in part, feature extractor implemented be equally applied, for instance, both to end-to-end classification models (e.g.. DER++ [9]) and to approaches with a neural backbone that computes rereesetpreen prototypes (e.g., CoPE [15]).
neural network. As such, the proposed method can
4 Performance Analysis
4.1 Experimental setup
Benchmarks. We build two OCL benchmarks by taking image classification datasets and splitting their classes equally into series of disjoint tasks:
Split Mini-ImageNNe [66, 13, 21, 17] that includes 100 classes from ImageNet, allowing for a longer task sequence. For each class, 500 images are used for training and 100 for evaluation.
Split FG-ImageNet! [58] is a benchmark for fine-grained image classification that we use to test CL methods on a more challenging task than traditional ones. It includes 100 classes of animals extracted from ImageNet, belonging to 7 different species, reducing inter-class variability and leading to harder tasks. Each class contains 500 samples for training and 50 for evaluation.
For both datasets, images are resized to x384 pixels and split into twenty 5-way tasks.
Baseline methods. We evaluate the contribution of the SER strategy when paired to a classification network trained using several state-of-the-art continual learning approaches, including rehearsal and non-rehearsal methods:
seminal work that combines rehearsal and knowledge distillation strategies for supporting model plasticity while limiting forgetting.
DER++ [7]:
ER-ACE [11]: variant of Experience Replay [54, 57] which aims prevent imbalances due to the simultaneous optimization of the current and past tasks by selectively masking softmax outputs.
CoPE [15]: a prototype-based classifier with experience replay, whose careful update scheme prevents sudden disruptions in the latent space during incremental learning.
LwF [36]: a non-rehearsal method that enforces model to preserve outputs of past model instances on new samples to limit forgetting.
oEWC [30]: non-rehearsal method that mitigates forgetting by selectively limiting the changes on weights that are most informative of past tasks.
Implementation details. We apply the SER strategy at five feature modulation points of ResNet- 18's architecture, namely, the outputs of the first convolutional block and of the four main residual blocks. In compliance with online learning, all models are trained for single epoch, using SGD as optimizer, with a fixed batch size of 8 both for the input stream and the replay buffer. Rehearsal methods are evaluated with three different sizes of the memory buffer (1000, 2000 and 5000). When applying SER, besides each method's specific training objective, we also optimize the saliency prediction loss L. from Eq. 3, with x = 1. Saliency is estimated using DeepGaze IIE network [37] as oracle.
When using SER, classifier C' and saliency predictor S are identical ResNet- 18 architectures, followed respectively - by a linear classification layer and a saliency map decoder (additional details are provided in the supplementary materials). While C' is trained from scratch, we employ a pre-trained saliency predictor S. consistently with neuroscience evidence showing that humans have selective attention already embedded in the brain [48]. For a fair comparison, in all our experiments feature extraction backbones of baseline methods are initialized the same pre-trained weights as S (except where explicitly stated). Care was taken to ensure that the set of OCL classes C did not semantically overlap with pre-training data, to prevent any contamination from the saliency predictor to the classification task. Specifically, S was pre-trained for 20 epochs on a subset of 100 ImageNet classes (disjoint from our two main benchmark datasets), using DeepGaze IIE as oracle. No class label information was used at this stage. All experiments were conducted on workstation with an 24-core
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CPU, 500GB RAM, and an NVIDIA A100 GPU (40GB VRAM). Results are computed using the Mammoth framework [9].
Metrics and evaluation. As a primary metric of OCL model performance, we report the final average accuracy as T Ei=1d0 aT where af is the accuracy of the final model on the test set of task т₃ Accuracy af can be computed in Learning (Task-IL) setting. the latter, we assume that a task identifier is provided to the model at inference time, simplifying the problem by restricting the set of class predictions for given sample. While Task-IL often depicted as a trivial scenario in recent literature [23, 64, 2]. we emphasize its usefulness, as it isolates the effect of within-task forgetting from the model's bias towards the currently learned classes [71, 25, 7]. In the paper, we mainly report results in Class-IL, while the results in Task-IL setting are given in the supplementary materials. Results are reported in term of mean and standard deviation over five different runs.
Learning (Class-IL) in Task- mnsk-encreeenall
4.2 Results
We first evaluate the contribution that saliency-driven modulation provides to state-of-the-ar OCL baselines. For each method, we compute Intremnncremttlll accuracy and compare to those obtained when integrating SER, as described in Sec. 3. Since our strategy foresees two paired networks for classification and saliency prediction, we also compare with similar multi-branch CL baselines:
DualNet [51], mentioned in Sec. 1, employs back-be architecture to decouple incremental classification (by a fast learner) from self-supervised representation learning [73] (by slow learner). We adapt SER to DualNet by replacing the slow learner and its training objective with our saliency prediction backhone, forcing the fast learner to use saliency features for classification. TwF [8] employs a frozen pre- pre-trained classification backbone stabilize the learning of Class- Incremental features, by means of an attention mechanism. To enable SER, the pre-trained classification backbone and the feature distillation strategy are replaced with the saliency encoder, and the features of the two backbones are combined through multiplication, as described in Sec. 3.
Results are reported in Table I, showing pattern of enhanced performance when integrating SER up to 20 percent points. In terms of comparison against two-paired networks, integrating SER outperforms both of them, suggesting that controlling learning through saliency leads better representation for classification than, for instance, contrastive learning (as done in DualNet) or feature attention with a pre-trained backbone (as TwF) This inline with cognitive neuroscience [19, 35], for which object identity-pree preservation, that also involves contrastive learning, happens mostly at later layers (c.g.. IT neurons), while selective attention (through visual saliency) acts during the whole categorization process. Results for non-rehearsal methods are reported in the supplementary materials.
4.3 Ablation Study
The proposed strategy is grounded on cognitive neuroscience literature, according to which selective attention modulates neuronal responses of all layers involved the categorization process, multiplicative fashion. Our next experiments modulation through multiplication for all classification layers) is optimal also for artificial neural networks, if other integration modalities of saliency information may be equally effective. We thus compare our SER strategy with the following baselines, all exploiting saliency information in different ways:
meant to assess whether this hypothesis (i.c., feature
Saliency-based input modulation (SIM): the input image is multiplied by the corresponding estimated saliency map (thus highlighting salient regions only).
Saliency as additional input (SAI): we modify the classification network to receive as input a 4D data tensor, with the saliency map concatenated to RGB channels.
Learning saliency-based modulation (LSM)): rather than multiplying classification features x and saliency features _ (see Eq. 4), we feed them to convolutional layer with 1x1 kernel to produce z! and let the model learn the corresponding parameters.
We could not TwF with buffer size of 5000, due excessive computing requirements.
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Table 1: Class-Incremental accuracy of SOTA rehearsal-based methods with and without SER.
Model
Split Mini-ImageNet
Split FG-ImageNet
Joint
14.79
9.06:11.07
Fine-tune
3.43
2.43:10.81
Buffer size
1000
2000
5000
1000
2000
5000
DER+
14.95+3.11
12.82::4.97
14.58+22.55
8.08+41.54
8.27+1.72
9.20++0.8
+SER
19.13=1.62
22.92=2.25
25.35
11.71+:2.66
12.97=1.62
13.73#1.95
ER-ACE
2..86
21.93:13.20
26.3115.22
14.28:10.96
16.45.1..4
18.21
SSER
27.4812..8
33.09 1.22
35.58 11.79
20.03
23.80 12.11
28.68 10.50
CoPE
21.58+11.60
23.38+4..9
24.77=3.56
16.45-+1.38
16.81+0.83
17.77+2.02
+SER
26.66=2.22
33.35 4.67
45.04
18.17:2.79
1..14
34.34
Dual-branch methods
TwF
23.7841.67
29.05:22.0
15.3242.50
18.72
SSER
28.36
35.55
20.04 1.63
22.54.2220
DualNet
20.57+00.1
27 19.71
12.08+1.55
15.62++1.4
21.04+1.08
22.07 08
SER
28.58=1.40
33.76=1.21
36.44
19.48+0.59
2..53+1.56
24.83=2.01
Split Mini-ImageNeet
Split FG-ImageNet
Cuesk Aconoonti
Qust, Aonenn
80
3
85
88
88
Figure 3: Comparison of SER to alternative saliency integration strategies. SIM modulates input images by saliency maps. SAI provides saliency maps as an additional input channel to the classification network. LSM merges classification and saliency features through learnable convolutional layer.
Fig. reports the results of this analysis, using DER+ and ER-ACE as baseline methods, and clearly indicates the superiority the SER strategy to other saliency integration variants. However, is interesting note that saliency helps classification performance in all cases, demonstrating its usefulness for continual learning tasks. We argue that this is due to the intrinsic nature of saliency prediction, which we found to be i..dd with respect to the data stream.
We then investigate whether the impact of seleenive-driven modulation uniform across the backbone layers. To this aim, we define positional binary coding scheme, controlling the application of the SER strategy the predefined points of the network (see Sect. 4. 4.1): if position scheme is 1, then the i-th feature modulation point is enabled, i.e., features from the i-th block of the classification network are multiplied by the features of the i-th block of the saliency network. Results are reported in Table 2 for both DER++ and ER-ACE, and indicate that the best strategy is to modulate the features of all classification layers through the corresponding saliency ones, similarly to what neurophysiological evidence reports [63, 45].
the coding
4.4 Model Robustness
We finally assess the robustness the SER strategy to spurious features and adversarial attacks. Spurious features are information that correlates well with labels in training data but not in test data

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Table 2: Performance comparison when applying SER to DER++ and ER-ACE different layers of the ResNet-18 backbone, with buffer size 2000 (Class-IL).
Split Mini-ImageNet
Split FG-ImageNet
SER Scheme
DER++
ER-ACE
DER++
ER-ACE
11100
12.97+2.62
23.72:0.77
6.5410.67
18.08=0.96
11110
17.46+1.02
26. 26.4442.33
8.77+1.45
16.55=2.55
11111
22.92:2.22
33.09=1.28
12.97=1.2
23.80+2.11
(e.g., in classification task between birds and dogs, training with yellow birds and black dogs only), leading to low generalization [34]. This effect is exacerbated in continual learning settings, where the covariate shift between train data and test data increases as new tasks come in. Thus, we measure to what extent our SER strategy can mitigate the tendency of learning methods exploit spurious features to solve classification tasks. We crafted an ad-hoc benchmark consisting of ten classes from ImageNet. For each class, we added class signature for training images, leaving the test images unaltered. In detail, we modified each training image by increasing the brightness of all pixels by class-dependent offset, computed as 5(c+11 1) (in 0-255 brightness range), where e numeric class label. We then define five continual learning tasks with two classes each. We then compare ER-ACE the corresponding SER leabbld variant and ground its performance with the one obtained when it is trained with original images (i.c., without enforcing spurious features in the data). Results Table show that SER effectively limits the possibility for classifier to use spurious features, resulting in more robust and generalizing model. The drop of performance (about 22 percent points) observed between training with the original data and training with data biased by spurious features is almost completely recovered when SER is used.
Finally, evaluate the robustness of SER against adversarial perturbations of the input space. To this aim, we apply the Projected Gradient Descent (PGD) attack [42] with different & values (determining the strength of the attack) and compare the average performance drop experienced by ER-ACE, in its original version and when combined with SER. We conduct the evaluation on both Split Mini-ImageNet and Split FG-ImageNet, repeating each experiment three times. As shown in Figure 4, SER considerably improves model stability, counteracting perturbations by regularizing classification features with saliency ones.
Supplementary materials include additional experiments: performance in Task-IL settings, results for buffer-free methods, effect of pre-training on a pre-text task for the classifier and saliency predictor backbones, cost analysis showing training and inference times of our approach compared to existing methods.
Accuracy (%))
Method
Class-IL IL
Task-IL
40
ER-ACE
50.0743.56
86.77+1.63
80
ER-ACEST
28.4643.d0 16
74.40+4.37
+ER
44.08 13.67
83.04
100
0.001
Figure 4: Robustness to adversarial attacks. ER-ACE baseline drops even with small attacks, while SER significantly enhances robustness.
Table 3: Effect of the SER strategy in the presence of spurious features. The S apex shows training on biased dataset with spurious features.

---

Conclusion
We presented SER, a paglooicalll-inspired saliency-driven modulation strategy for online continual learning, which regularizes classification features using visual saliency, effectively reducing for- getting. The proposed approach, grounded on neurophysiological evidence, significantly improves performance of ffite-of-the-rrt OCL methods, and has been shown be superior to other multi- branch solutions, either mooooically--nspiin (e.g., DualNet) or based on attention mechanisms (e.g., TwF). Our results confirm that adapting neurophysiological processes into current machine learning techniques is a promising direction to bridge the gap between humans and machines.
Limitations and future works. In this work, we introduce the use of saliency maps auxiliary knowledge to mitigate forgetting in continual learning. This involves pre-training our saliency predictor with oracle, which could be in the form of either ground-truth maps generating pseudo-labels. High-quality input images are necessary for producing meaningful saliency maps, thus, datasets like LIFAR10/10) cannot be employed due to their lower resolution.
external model
Although SER is model-agnostic, its formulation necessitates that the saliency encoder and the classifier share identical architectures. To apply this to heterogeneous networks, will explore defining or learning mappings between activations at different network stages.
Finally, our finding that saliency prediction is i.i.d. with respect to classification distribution shifts opens the door investigating whether other low-level visual tasks share this property.
6 Acknowledgements
G. BBlllitto, Proietto Salanitri and C. Spampinato acknowledge financial support from PNRR MUR project PE0000013-FAIR M. Pennisi is a PhD student enrolled in the National PhD in Artificial Intelligence, cycle XXXVII, course on Health and life sciences, organized by Università Campus Bio-Medico di Roma.
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A Supplementary Materials
A.1 Architectural Details of the Saliency Prediction Network
1389 98G
Skip-1
Inout
pis 19 se
Prediction
Kols 144
Encoder RedVet18
Post-orn
Ussomp Ing
Upoormph
Post Upsompiing:2
Fuskon
Bi 366
[B2 30
pa 64)
[138
(322 (0 Lot
(ou
[I 30
Figure SF-1: Overview of the Saliency Prediction Network used for our experiments
For experiments create ad-hoc encoder-decodee saliency prediction network with skip connections. This network uses ResNet-18 as encoder as to be similar to the paired classifier, thus easing the saliency-based modulation between the two branches.
The saliency decoder is instead broadly inspired by UNISAL[20]. We opted for UNISAL decoder because of the low number of parameters it requires, which leads to short runtime if compared to other saliency models'. In particular, the decoder consists of a stack of pointwise convolutions and deptwhise separable 3 convolutions, interleaved with bilinear upsampling blocks until the size of the original input image is recovered, while features from second and third residual blocks of the Encoder are used as skip connections, through two modules named Skip-2 and Skip-4, to fuse features extracted at different abstraction levels. The architecture of the proposed model is illustrated in Fig. SF- Essentially, features from the bottleneck are upsampled with factor and concatenated with the output of Skip-2 module. The obtained features maps are upsampled and concatenated with the output of Skip-4 module, while the number of
again with factor feature maps is progressively scaled from the original value of 512 to 64. One last 1 x convolution, followed by an upsampling layer and logistic activation, reduces the feature maps and the spatial sizes are restored to those of the input image. More details are reported in Table ST-1.
Table ST-1: Detailed input-output sizes of the Decoder of our Saliency Prediction Network
Saliency Model: Decoder
Name
type
kernel/(stride)
Batch Norm
Activation
Input shape
Output shape
SepConv2D
3 (/(33)
Yes
ReLU
512
36
512
36
Poxt-cno
Conv2D
3 3/
Yes
512
36
48
256 x
36
48
Upsampling-1
Upsumple
256
36
48
256 x
72
96
Skip-2
Conv2D
x 1/(11)
Yes
ReLU
256
72
96
512 x
72
96
Conv2D
1 1/(1,1)
Yes
512 x
72
96
512 x
72
Conv2D
x 1/(1.11)
Yes
ReLU
384 x
72
96
768 x
72 x 96
Upsampling-2
SepConv2D
3/(1))
Yes
ReLU
X
72
Conv2D
1x 1)(1,)
Yes
768
72
96
128
72
96
Upsumple
2
768 x
72
96
128 X 144
192
Skip-4
Conv2D
1x(1
Yes
ReLU
125 x 144
192
256 X 144 X 192
Conv2D
1x(1(1
192
64 X 192
Conv21)
1/(1,1
ReL.U ReLU
192
X 192
Post- Mhst-Upsampling-22
SopConv2D
3 3/(1(1)))
Yes
384 x 144
192
384 X 144 192
Conv2D)
1 1x(1,)
Yes
384 x 144 192
64 x 144 192
Conv2D
1 1/(1,1)
Sigmoid
64 x 144 192
1 X 144 x 192
Fusion
Upsample y
X 144 192
x 288 384
'A comprehensive comparison between performance, number of parameters and execution runtime of the most recent saliency models can be found at: https: http:///nnnhnnnnce/eeee www.://gg...teeeve...eaaaaass.oa
15

---

A.2 Additional experiments
A.2.1 Additional Comparison with Recent CL methods
We further extend the performance analysis by comparing SER strategy other prominent CL methods in the glass-Incremental Learning setting, including recent approaches explicitly designed for Online CL, such as PEC [72] and OnPro [70]. As shown in Table ST-2, methods trained with the SER strategy (last three rows, as previously presented in Table of the main paper) outperform existing methods by several percentage points, confirming the effectiveness of our approach compared to recent OCL strategies.
We also report the results obtained with TASS [38], a prior work that share some similarities with our SER method, as it introduced the use of attention maps in CL.. However, these are significant differ- ences between the two approaches. First, TASS employs statie, pre-trained saliency detector, which does not showcase the forgetting-free free capabilities of saliency prediction since it is not continuously trained, unlike SER. Additionally, TASS number of training epochs per task (100) and, in its original implementation, uses 50% of the classes the first task.
designed for the OCL scenario, as it requires large
Table ST-2: Comparison with SOTA methods, in terms of Class-Il. final average accuracy (FAA).
Method
Split Mini- Mini-ImageNet
Split FG-ImageNet
Buffer-free methods
PEC [72]
14.87
12.58+0.54
TASS [38]
6.87+2.17
5.49+0070
Rehearsal-based methods
Buffer size
1000
2000
5000
1000
2000
5000
ER [56]
14.51 15.55
16.8512.32
19.7311.48
10.41
6.67
10.00
A-GEM [12]
3.27
3.57:0.47
3..1
3.5010.22
3.5010.34
3.60
BiC [71]
7.50
9.364 033
9.53+1..3
4.87+0.52
4.73+11.4
4.65=20.22
FDR [5]
3.36=0.28
3.78+0.06
3.76+0.35
3.30+:0.06
3.2740.04
3.15=0.13
GEM [41]
5.45=0.14
5.92+1.10
5.76+0.51
3.1740.1 10
2.5940.13
3.43=0.04
GDumb [52]
16.14=0.18
24.12:+0.96
38.67+0.04
11.95++0.16
19.29+1.74
32.19=1.51
GSS [2]
7.88=2.61
11.18+1.30 30
9.38+
7.78+0.88
6.41+0.21
9.07=0.35
iCaRL [55]
15.64=0.13
15.81+0.53
14.58+1.58
8.97+0.66
9.32+0.03
8.84=0.76
LUCIR [25]
8.77=1.12
12.1442.06
17.23+1.100
5.00+
5.47+0.19
5.40=0.59
RPC [50]
17.14 3.77
20.08
21.0011.46
9.9611.99
9.32
9.29 1.12
OnPro [70]
19.34 0.26
24.2910.67
32.23:1.55
11.73
15.6310.13
19.95
DER++ + SER
19.13=1.62
22.92+22.25
25.35+22.66
11.71+2.36
12.9711.62
13.73=1.95
ER-ACE SER
27.48 22.88
33.09+1.28
35.58+1.79
20.03++.18
23.80+2.11
28.68=0.50
CoPE + SER
26.66=2.22
33.35+4.67
45.04+2.41
18.1742.79
27.14+1.62
34.34=3.51
A.2.2 Task-Incremental Learning setting performance
Table ST-3 reports the fask-Incremental accuracy of OCL baselines alone and when integrated with SER.
A.2.3 SER with Buffer-free methods
In Table ST-4 we report the results for both Class-Incremento and fack-mnncemnntal settings using two common buffer-free methods: LwF [36] and oEWC [30]. Applying SER leads to performance improvements in both cases. In this case, the improvements are more evident for Task-Incremmettal: marginal gain Class-Incremental also noticeable, though the low performance of the baseline methods limits the room for improvements.
16

---

Table ST-3: lisk-Incremental accuracy of state-of-the methods with and without SER.
Model
Split Mini-ImageNNt
Split FG-ImageNet
Joint
63. 12
56.33+2.51
+SER
64. 1000000
56.72.
Fine-tune
34.08.1.225
28.81 1.166
+SER
57.07:13.44
51.24
Buffer size
1000
2000
5000
1000
2000
5000
DER+
73.07+3.07
15.61
77.7113.04
68.65
70.24+3.97
74.74+1.14
+EER
79.75+1.56
82.97=0.25
84.10=0.80
72.83
75.40+2.22
78.26+1.10
ER-ACE
71.00+3.21
75.60
17.17414.08
66.27:10.9
69. 09.0941311
70.88+55.72
+SER
77.51
82.22=0.90
83.56=1.60
73.08=2.14
75.60
79.46=t0.56
CoPE
08.0010.73
71.76
74.31:12225
63.77:12.32
67 13.39
69. 1...93
+SER
72.69 10.80
77.57 11.7
84.64 11.20
64.79 1.66
13.39.1.11 11.11
78.6611..55
Dual-branch methods
TwF
1..57
18.38=1.60
54.32
72.15+2.82
SER
09.28.12.22
82.98 20.85
71.35 1.77
73.34
DualNet
00.5
76.49
30.26:t0.00 97
67. 1..50
71.5440.72
74.53+1.27
SER
81.79
83.79 .0.27
85.72.10.40
10.56
78.35
80.18110.55
Table ST-4:
Class-Incremental and Task-Incremental accuracy of non-rehearsal methods with and without SER.
Split Mini-ImageNet
Split FG-ImageNet
Model
Class-II.
Task-IL.
Class-ll.
Task-IL
Joint
14.79
63.12.1..
9.06
56.33
+ER
16.26=0.30
64.18 =0.60
9.51
56.72=1.09
Fine-tune
3.43
34.08 12.28
2.43
28.81
SER
4.20
57.07 +3.44
3.68#0.44
51.24=2.36
LWF
3.18
30.61+1.80
3.25=+0.4
27.55=1.64
SER
4.22
48.61 14
3.57
$6.57=2.00
oEwC
2.68+0.24
24.10+1.65
2.38#0.23
24.98=1.16
SER
3.08
35.33
2.55
26.02
A.2.4 Effect of classification pre-training
In Table of the paper have reported the results of experiments when the classification backbones the baseline methods are initialized to the same weights as the saliency encoder, for fair comparison. In this section, order to demonstrate generalization capabilities of the SER strategy, and to ground our approach to the CL methods that exploit pre-training, we also compute performance when the classifier backbone and saliency encoder are pre-trained features appears to be in contrast to what it
classification
pre-text task (despite using happens in the human brain). Differently from what described Sec. 4.1, here we use the same disjoint subset of ImageNet classes to train the backbone of the classifier, then we initialize the saliency encoder to the same weights. Also in this setting, methods combined to SER achieve better results, as show in Table ST-5. However, the performance gain is lower than the one obtained with saliency pre-training. This is possibly due the fact that classification pre-trained features are better than saliency ones (as also evidenced by the general higher performance obtained with classification
ceseffication-peetrain
17

---

pre-training) and have reached their maximum capacity. These results confirm again the contribution of the forgetting- free behaviour of the saliency prediction task classification tasks.
Table ST-5: Class-IL and Task-IL performance when the classifier backbone and saliency encoder are pre-trained on a classification task with classes different from those available in the continual learning settings.
Model Buffer
Split Mini-ImageNe
Split FG-ImageNet
1000
2000
5000
1000
2000
5000
CLASS-IL
CLASS-IL
DER++
30.35=0.74
30.96++0.9
32.55+1147
06.708
16.21
16.83=0.41
+SER
31.20 2.39
33.91 12.31
37.91
17.06
20.43
22.53
ER-ACE
42.33 10.57
10.54
18.7711.28
30.91
34.09
37.49
+SER
46.5611.10
50.52.10.
53.23
32.4611.09
36.08
40.73
TASK-IL
TASK-I
+
89.98=0.76
91.14 20
91.37+0.10 10
83.87
85.6 : 29
86. $6.19920.2
+EER
89.3440.54
90.47:10.32
91.36+:0.00
82.34
84.04+0.40
84.83=0.80
ER-ACE
88.2840.50 90.1440.05
91 91.23+0.13
82.83+00.00
85.39
87.29
+ER
80.99
90.83+0.20
91.84+0.08
82.94=1.115
84.25=0095
86.51=0.25
A.2.5
Backbones Comparison
We performed other experiments including alternative backbones beyond the classical ResNet- 18 evaluate the generalization capability of SER across different architectures. Specifically, we applied our SER strategy with ResNet-50, MobileNet V2, and DenseNet-121. For each backbone, we compare the results obtained with the ER-ACE method with buffer size 1000, in three scenarios: when the backbone is trained from scratch, when it is fine-tuned, and when SER is applied. As reported in Table ST-6, in all cases our SER approach leads to improved performance, thereby demonstrating its effectiveness.
A.2.6 Saliency Prediction in CL settings
KLD
magelee
334
0.40
0.4
112
0-2
345
0.0
5pit mit min.rageliee
0.75
F0-irageles
0.55
7%5
Figure SF-2: Saliency prediction accuracy, measured in terms of Similarity (SIM), Pearson's s Correlation Coefficient (CC) and Kullback-Leibler divergence (KLD) metrics, in continual learning settings on the Split Mini-ImageNeet and Split FG-ImageNet benchmarks.
Table ST-6: Class-II performance ER-ACE using different backbones.
Split Mini-ImageNNet
Backbone
not pre-trained
pre-trained
ER-ACE+SER
ResNet18
15.71
20.86+3.69
27.48=2.83
ResNet50
13.38
20.34:2.72
32.16=1.23
MobileNet V2
12.76-10.44
16.55
17.77=0.41
DenseNet121
15.47=0.62
18.68+1.85
21.03=0.u5
18

---

We here report quantitative performance of estimated saliency in CL settings. Fig SF-2, in particular, shows the forgetting- behaviour of saliency predictions: Pearson's 's Correlation Coefficient (CC), Similarity (SIM) and Kullback-Leibler divergence (KID) (metrics commonly employed for saliency predictions [10] do not degrade with the number of CL. tasks.
A.2.7 Cost Analysis
Table ST-7: Comparison of training and inference times and parameters between SER, DualNet
and TwF.
Metric
DualNet [51]
TwF [8]
SER
Train params
M
58 M
23 M
Train time
6.5 h
~ 3.0 h
1..
Inference params
16 M
11 M
22 M
Inference time
3.45 ms
3.15 ms
7.50 ms
Table ST-8: Training time for the competitor methods, in their standard version, and when our SER strategy is applied.
Model
baseline
+ SER
LwF
< 1.0h
~ 1.5 h
oEWC
1.0h
1.5 h
DER++
~ 0.5 h
1,0
ER-ACE
0.5 h
1.0
CoPE
2.0
~
h.5
We perform cost analysis to assess the efficiency of our SER approach compared to existing methods that employ two branches, i.e., TwF [8] and DualNet [51]. It is important note that in continual learning settings, efficiency at training times might be more relevant than the one at inference times the main assumption is of deep model that keeps training from infinite stream of data. The comparison is carried out using the ResNet18 backbone for all models. The results in Table ST-7 reveals that SER is much more efficient than DualNet and TwF at training time, while it shows higher costs at inference time (but also an accuracy gain of ~~ points).
Additionally, Table ST-8 we report the training times of the baseline version of the competitor methods, and when integrated with SER. Training time is approximately the same on both datasets, as they consist of an equal overall number of images, and the size of the buffer has negligible impact on the training time.
19

---

A.3 Reproducibility Details
A.3.1 Hyperparameter Search
In Tables ST-9 and ST-10 we show the best hyperparameters combinations for each method.
Table ST-9: Split Mini -mag-em
Method
Buffer
Spli--MinilmageNet
SGD
Ir: 0.1
LwF
Ir: 0,01 alpha: 3.0 softmax temp: 2.0 wd: 0.0005
OWW
Ir: 0.03 e lllandd:: 10 gamma: 1.0
DER++
1000
Ir: 0,01; alpha: 0.1; beta: 0.5;
+
2000
Ir: 0.01; alpha: 0.1; beta: 0.5;
+
5000
Ir: 0.01; alpha: 0.1 beta: 0.5
ER-ACE
1000
Ir: 0.01; mom: wd: 0
ER-ACE
2000
Ir: 0,01; mom: 0 wd: 0
ER-ACE
5000
Ir: 0.01; mom: O wd: 0
CoPE
1000
Ir: 0.01; hidden_dim: 256; loss _oss___000;; p_momentum:00...
CoPE
2000
Ir: 0,01; hidden Bidden_dim: 256; loss T:0.05; p momentum::.
CoPE
5000
0.01; hidden dim: 256; loss T:0.05; 100.05:p_monnie momentum:0.9;
TwF
1000
Ir: 0.01; dier_alpha: 0.3; der beta:0.9;
TwF
2000
0.01; der_alpha: 0.3; der _er_bet::0.9;
DualNet
1000
Ir: 0.01; n_outer: 3; n_inner: 2; temp_reg 2; alpha_ree regg 10.0 slownet_ _bta: 0.05
DualNet
2000
0.01; n_outer: 3; inner: 2; temp_reg _ee_rreee_ 2; alpha_regg [egg 10.0 slownet_ beta: 0.05
DualNet
5000
Ir: 0.01; n_outer: 3; ___nner: 2; temp_reg reg 2; alpha_reg: 10.0 slownet_ beta: 0.05
Table ST-10: Split FG-ImageNet
Method
Buffer
Split FG-ImageNet
SGD
0.1
LwF
Ir: 0,01 alpha: 3.0 softmax temp: 2.0 wd: 0.0005
OEWC
0.03 e_lambda: 10 gamma: 1.0
+ER++
1000
Ir: 0.01; alpha: 0.1; beta: 0.5;
+ER++
2000
Ir: 0.01; alpha: 0.1; beta: 0.5;
DER+
5000
Ir: 0.01; alpha: 0.1 beta: 0.5
ER-ACE
1000
Ir: 0.01; mom: O wd: 0
ER-ACE
2000
Ir: 0.01; mom: 0 wd: 0
ER-ACE
5000
Ir: 0.01; mom: wd: 0
CoPE
1000
0.01; hidden dim: 256; loss T:0.05; p_ momentum:0..9
CoPE
2000
Ir: 0.01; hidden_diing 256: loss _oo.ST; p_ wwm.mmmmmmmm.999
CoPE
5000
0.01; hidden_diine 256; loss To0.055 p__momnntmm0..9
TwF
1000
Ir: 0.01; der_alpha: 0.3; dde__bet::0.9
TwF
2000
0.01; der der_alpha: 0.3; der beta:0.9;
DualNet
1000
Ir: 0.01; n_outer: 3; j__inerr 2; temp_ sem__reg- 2; alpha_rep reg: 10.0 slownet__bele 0.05
heppha_reg:_
DualNet
2000
0,01; n_outer: 3; inner: 2; temp_reg 2; alpha aipha_reg: reg: 10.0 slownet beta: 0.05
DualNet
5000
Ir: 0.01; n_outer: 3; iniiner: 2; temp_reg 2; alpha_reg: 10.0 slownet_bett 0.05
A.3.2 Task sequence details
In Tables ST-11 and ST- 12 we report the combination of class order and their division into tasks employed our experiments during the continual training. Each name corresponds to different synset of the ImageNet dataset.
20

---

Table ST-11: Split- MolitMMinllmageNet
Task
Synsets
TI
n02091244
n0177008
n03207743
n01749939
n02110063
n02174001
n02165456
n02687172
n09246464
n02871525
73
n01855672
n03062245
n04149813
n04067472
n04522168
74
n02138441
n04509417
n04275548
n03888605
n01981276
55
n02091831
n03400231
n02219486
n02795169
n03773504
T6
n03337140
n01558993
n03998194 194
n02129165
n03127925
77
n02457408
n02108915
n04389033
n04604644
n03908618
n02443484
n02116738
n03854065
n03544143
n09256479
Tg
n04251144
n02606052
nO2113712
n02950826
n07747607
710
n02108551
n02108089
n07613480
n03527444
n02823428
T11
nO1532829
n02981792
n02120079
n03476684
n03047690
712
n02971356
n02074367
n06794110
n04612504
n03924679
T13
n01910747
n02105505
nO3584254
n03770439
n01930112
T14
n04435653
n03347037
nO3535780
n04243546
n04596742
T15
n02099601
n04418357
n02089867
n03272010
n03220513
T16
n04146614 14
n04443257
n02111277
n02747177
n04515003
717
n13054560
n01843383
nO7584110
n13133613
n04258138
718
n03075370
n02966193
n03417042
n03146219
n03838899
T19
n03775546
n03017168
n03980874
n02114548
n03676483
720
n01704323
n07697537
n02101006
nO4296562
nO21 10341
Table ST-12: Split FG-ImageNet
Task
Synsets
T1
n01943899
n01753488
n01819313
n01601694
n01695060
T2
n02028035
nO1675722
n01498041
n01774750
n01608432
T3
n01685808
n01978287
n01537544
n01742172
n01924916
n01829413
n
n01494475
n01877812
n02027492
76
n02058221
n01491361
n01910747
n01729977
n02018207
T6
nO1824575
n01986214
n01860187
n01773797
n01630670
T7
n01796340
n01687978
n01984695
n01729322
n01833805
n01776313
n01443537
n01560419
n02018795
n01985128
Tg
n01677366
n
nO1739381
n0177008
n02013706
710
nO1978455
10
n01514668
n01440764
n01 855672
T11
n01756291
nO1770393
n01775062
n01632458
n01820546
712
nO1496331
nO1582220
n01734418
n01622779
nO1632777
T13
n01806143
n01773549
nO1774384
n02077923
n01740131
T14
n01484850
n01914609
nO1665541
n01667778
n01847000
T15
n01667114
n01728572
nO1693334
n01843383
n01950731
716
n01514859
n02012849
n01773157
n01614925
n01795545
T17
n01944390
142011460
nO1883070
n02002556
n01798484
T18
n02051845
n01644900
nO1531178
n01968897
n01698640
T19
n01592084
n01955084
n01930112
n02007558
n01735189
720
n11751748
n01664065
n01749939
n02006656
n01828970
21

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Table CL-1: Assets used and licence information.
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Github/ / URL
Cllation/Reference
Mammoth
code
MIT
aimagclab/mammotth
[9]
UNISAL
code
Apache-2.0
ndroste/unisall
[20]
Split MinitmageNet
data
non- nom-commeraial research
[66]
Split FG Net
reseurch
https///ww..oo hww.a//.......uuueeeeeaaaaaaaaoo
[58]
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Reviews:
[{"review_id": "gFp7MW8F2j", "reviewer": "Reviewer_U9b5", "summary": "The paper propose to use saliency prediction features as a guidance to stabilize training in online continual learning settings. The method is motivated by the observation that saliency detection remains stable with training over new tasks continually. The proposed SER method is model-agnostic and improves significantly over baseline methods. Interestingly, the method enables training of models which are more robust to adversarial attacks.", "questions": "See weaknesses", "limitations": "Addressed in paper.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper has insightful experiments to form the motivation. The distinction between saliency maps and attention maps is helpful.\n2. The method explanation and illustration is clear and to the point.\n3. The experiments (combining with different methods) and extensive ablations are appreciable.\n4. The analysis showing robustness of the model adds more value to the method.", "weaknesses": "1. Fig 1. Caption “activation maximization maps via GradCAM, which are prone to catastrophic forgetting due to their dependence on the classifier”: I am not sure if this statement is true. This should be discussed in more details. Existing Attribution methods like GradCAM, Integrated Gradients can also be computed for different layers of the network and is not only for the classifier. It would be good to investigate how methods like LayerIntegratedGradients [1] work here, is that still dependent on the classifier? Layer Attributions has also been explored for continual learning [2] recently. While the authors discuss methods using attention maps for future replay, it is also relevant to discuss methods like [2] which uses attributions for weight transfer/transfer learning.\n\n2. It would be good to include more recent methods in comparison. The compared methods for OCL although important baselines, are not very recent ones.\n\n[1] https://captum.ai/api/layer.html\n\n[2] Goswami, Dipam, et al. \"Attribution-aware weight transfer: A warm-start initialization for class-incremental semantic segmentation.\" Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the provided comments. We here address point by point the weaknesses (indicated with W1 and W2) identified by the reviewer.\n\n**W1 - Activation maps per layer**\n\nWe have tested GradCAM at different layers of the network, as well as LayerIntegratedGradients, as suggested. Our results, shown in Figures R1 and R2 of the attached PDF to the global rebuttal, reveal that activation maps from deeper classification layers, as well as intermediate and lower layers, degrade as the model is trained continuously. This indicates that catastrophic forgetting affects not just the classifier, but multiple layers of the network. Using LayerIntegratedGradients, we observed that the features learned by the network tend to change from task to task. This change leads to the forgetting of initially learned classes and concepts, demonstrating that this method also depends on the classifier and is susceptible to forgetting.\n\nAs for the significance of layer attributions in continual learning, we would like to clarify the differences between our method and the one suggested (and indicated with [2]) by the reviewer. SER is specifically designed for class-incremental learning in image classification. It employs saliency prediction techniques to modulate classification features during training. We have demonstrated that saliency prediction remains robust during training and is less prone to degradation, ensuring better performance and stability over time.\n\nIn contrast, the method described in [2] addresses class-incremental semantic segmentation (CISS) and focuses on forgetting in semantic background shift. Their approach introduces a novel classifier initialization technique that uses gradient-based attributions to identify and transfer relevant weights for new classes, specifically to address background shift in segmentation tasks.\n\nThe key difference lies in our use of saliency prediction techniques versus [2]’s use of gradient-based attribution for weight transfer. Our approach does not rely on attribution techniques, which we have shown to degrade during training, but rather on saliency prediction, which is resilient to forgetting.\n\nHowever, we appreciate the opportunity to clarify these differences and we will include them in our revised manuscript, acknowledging [2]'s contribution to the field and discussing its relevance to our work.\n\n**W2 - Comparison with more recent OCL methods**\n\nWe have extended our comparison to include more recent methods (see Table R1 of the pdf attached to the global rebuttal). Specifically, we have included several existing methods already integrated within the Mammoth continual learning framework (*Boschini et al, 2022*). This framework offers a common and consolidated benchmark for online and offline continual learning methods, which simplifies the comparison of performance and the assessment of each method’s contribution.\n\nAmong the tested methods, there are very recent approaches specifically designed for Online Continual Learning (OCL), such as PEC ([R1] in the global rebuttal), and OnPro ([R13] in the global rebuttal). Results show that methods trained with the SER strategy outperform both their counterparts without SER and existing methods by several percentage points, demonstrating the effectiveness of our SER strategy.\n\n- Boschini et al, 2022. “Class-Incremental Continual Learning into the eXtended DER-verse,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.", "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": "YenLKmVuqV", "reviewer": "Reviewer_L6ph", "summary": "This paper draws inspiration from neurophysiological evidence and proposes a biologically-inspired saliency-driven modulation strategy named SER to mitigate catastrophic forgetting in online continual learning. SER works by regularising classification features via predicted saliency and is comprised of a classification encoder and a saliency encoder deployed in parallel. The proposed method produces superior results on image classification in both class-incremental and task-incremental settings compared to other biologically-inspired or attention-based solutions. Besides, it has also been shown to be more robust in detecting spurious features or adversarial attacks.", "questions": "1. In Figure 3, it is not clear what “SAM” refers to. Also, in Figure 4, why is the proposed method dubbed “SAM”?\n2. How sensitive is SER’s performance to other hyperparameters involved? More ablative experiments on the hyperparameters are beneficial.\n3. The authors argued that SER generalizes to networks of different architectures. It would be interesting and perhaps important to back their claim with experiments on Transformer models.\n4. The authors limited the scope of their problem from the very beginning of the paper to image classification tasks. The authors are encouraged to explain whether their method works for tasks such as semantic segmentation and object detection and whether these tasks are considered in prior works.", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1) The paper is well-written and easy to follow, with well-justified motivation and nice visuals. \n2) The idea of using forgetting-free saliency to combat forgetting in online continual learning is very simple and easy to implement. It has also been shown to be effective, as the proposed method improves different existing methods by notable margins under different settings. \n3) In addition to task performance, the authors examined the proposed method's computational cost, which suggests that SER is deployment-friendly.\n4) The authors also made a good attempt to delineate their motivation from a biological and neurophysiological perspective.", "weaknesses": "1) The authors conduct all experiments on ResNet-18 models. It is not clear if the proposed method generalizes to other models as well. Since the authors claimed their method's generalization property as an advantage, experiments on different datasets, tasks (e.g., segmentation, detection), and, most importantly, different architectures (such as ViT) are missing. \n2) Given 1), the authors fail to present results or investigation on cases when a) both the saliency encoder and the classifier encoder are ViT b) one of them is CNN-based and the other is transformer-based. This weakness is also acknowledged in the last sections of the manuscript.\n3) The method can be explained in greater detail. Currently, the authors spend about 1 page describing their own method out of 9 pages of the main text. While this is also due to the method's simplicity, I would suggest expanding this part by adding more detailed descriptions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their insight. We first address the weaknesses (indicated with W1, W2, and W3) identified by the reviewer and then respond, point by point, to their raised questions (indicated with Q1, Q2, Q3, and Q4).\n\n**W1 - Generalization to other models:**\nTo address this concern, we have extended our experiments to include additional backbones beyond ResNet-18. Specifically, we have tested our SER strategy with ResNet-50, MobileNet V2, and DenseNet-121. As reported in Table R2 in the PDF attached to the global rebuttal, in all cases, our SER approach leads to improved performance, thereby demonstrating its effectiveness across various architectures. Regarding the ViT backbone, please refer to our next response (to W2) where we provide both conceptual and practical reasons for excluding ViT from our analysis. \n\n**W2 - Generalization to transformer models:**\nSER focuses on emulating visual cortex mechanisms, particularly object recognition via selective attention, which aligns more closely with Convolutional Neural Networks (CNNs) due to their similarity to the visual cortex’s hierarchical and localized processing. CNNs have local receptive fields and hierarchical stages similar to those in the primate visual cortex, as detailed by Hubel et al. (1962) and DiCarlo et al. (2007). Empirical studies show CNNs’ effectiveness in predicting neural responses and modeling visual encoding in primates (Yamins et al., 2016; Cadena et al., 2019), reinforcing their suitability for selective attention and low-level visual processing.\n\nWhile Vision Transformer (ViT) excels in capturing global context and dynamic attention, it does not align with the hierarchical, localized processing of CNNs. Additionally, ViT requires large amounts of data and extensive training. Our experiments showed that ViT achieved only 1% accuracy on the Split-MiniImageNet benchmark, regardless of whether trained for 1 epoch or 50 epochs, underscoring its limitations in the context of online continual learning.\n\nHybrid solutions that combine CNN-based and Transformer-based models pose significant challenges due to the need for aligned semantic representation of features. SER relies on modulating classification features with saliency features. This modulation requires that the semantic representation of these features be coherent and aligned. Using hybrid solutions can disrupt this alignment, as CNNs and Transformers process and represent features differently. \n\nThus, our preference for CNNs over ViT and hybrid models is driven by the need to closely mimic visual cortex processing and by empirical evidence of low performance in OCL by ViT. We will revise our claims on SER’s applicability to continual learning methods.\n\n- Hubel et al., 1962. “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex,” Journal of Physiology.\n- DiCarlo et al., 2007. “Untangling invariant object recognition,” Trends in Cognitive Sciences.\n- Yamins et al., 2016. “Using goal-driven deep learning models to understand sensory cortex,” Nature Neuroscience.\n- Cadena et al., 2019. “Deep convolutional models improve predictions of macaque v1 responses to natural images,” PLoS Computational Biology.\n\n**W3 - More detailed description of the method:**\nWe acknowledge the request for a more detailed description and will provide additional details in the revised manuscript.\n\n---\n\n**Responses to Questions:**\n\n**Q1 - SAM:**\nIt was a typo that will be corrected in the final version of the paper.\n\n**Q2 - Sensitivity of SER to hyperparameters:**\nThe SER hyperparameters include: 1) the $\\lambda$ term that weights the loss component in Equation 3 of the manuscript, 2) the various saliency modulation strategies (ranging from saliency provided as input to the model, to layerwise summation, to layerwise multiplication), and 3) the specific layers to which SER modulation is applied. The latter two aspects have been thoroughly evaluated in the paper (see Fig. 3 and Table 2). Regarding the $\\lambda$ term, we tested several values ($\\lambda = 0.1, 0.5, 1, 1.5, 2$). Although the model's performance was relatively stable across these values, the best results were achieved with $\\lambda = 1$. Additionally, the PDF attached to the global rebuttal details the various tested hyperparameters (Table R3) for the methods reported in Table 1 of the manuscript.\n\n**Q3 - Generalization to transformer models:**\nPlease refer to our response to W2.\n\n**Q4 - Limited scope to image classification:**\nOur approach specifically addresses the problem of class-incremental learning (CIL) that targets image classification within continual learning settings. This particular focus aligns with a substantial body of existing literature and remains an unsolved challenge in the field. Indeed, for class-incremental continual learning, there are well-established frameworks such as Mammoth and Avalanche and widely recognized benchmarks, which we have employed in our work.\n\nWhile dealing with continuous training for tasks like semantic segmentation and object detection is an important and related area of research, it falls under other broader categories (e.g., incremental object detection - Liu et al. 2023 - and incremental semantic segmentation - Michieli et al. 2019). These tasks have different baselines, methods, and benchmarks. Expanding our approach to include these other tasks would require a separate and comprehensive investigation.\n\nHowever, class-incremental learning can be adapted to object detection, for instance, in the region proposal step, while it is less trivial to use in semantic segmentation. Nonetheless, we acknowledge the importance of extending continual learning methodologies to other tasks and consider it a valuable direction for future research.\n\n- Liu et al., 2023. Continual Detection Transformer for Incremental Object Detection. CVPR 2023.\n- Michieli et al., 2019. Incremental learning techniques for semantic segmentation. ICCV 2019.", "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": "2O8EUWTcXn", "reviewer": "Reviewer_H211", "summary": "In this paper, the authors propose Saliency-driven Experience Replay (SER) a biologically-plausible approach based on replicating human visual saliency to enhance classification models in continual learning settings. More concretely, they propose to employ auxiliary saliency prediction features as a modulation signal to drive and stabilize the learning of a sequence of non-i.i.d classification tasks. SER is model-agnostic and can be used in combination to any continual learning method. The authores demonstrate that saliency modulation positively impacts classification performance in online continual earning settings, leading to a significant gain in accuracy (up to 20%). Furthermore, they show that saliency modulation leads to learn saliency-modulated features that are more robust to the presence of spurious features and to adversarial attacks.", "questions": "Here are my concerns:\n- In the following statement (lines 138-139): '... for instance, class labels from $D\\_i$ might be different from those from $D\\_j$ , though both must belong to the same domain $Y$'. The most common setting in continual learning literature considers that the tasks $D\\_i$ and  $D\\_j$ are disjoint. According to your statement, it could be understood that they might not be. Only recently, in the reference below, the authors considered a novel setting, 'Class-Incremental with Repetition', which opens up the possibility that the tasks are not disjoint. Do you consider also this possibility in your approach? Please provide more details on this aspect.\n\nHamed Hemati, Andrea Cossu, Antonio Carta, Julio Hurtado, Lorenzo Pellegrini, Davide Bacciu, Vincenzo Lomonaco, Damian Borth. Class-Incremental Learning with Repetition. CoLLAs 2023\n\n- Lines 323-326, when you talk about the robustness of your approach against adversarial perturbations. Could you better explain the content of figure 4? What means SAM-ER-ACE (orange line) means? To which dataset these curves belong: Split Mini-ImageNet or Split FG-ImageNet?\n- A similar question for Table 3: to which dataset these values correspond: Split Mini-ImageNet or Split FG-ImageNet?", "limitations": "One limitation identified in the paper is related with SER: although it is model-agnostic, its formulation necessitates that the saliency encoder and the classifier share identical architectures. The current work does not have any negative societal impact.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is scientifically motivated, presenting a biologically plausible approach\n- The paper is clearly written and well documented\n- The related work section most of the relevant work\n- The experimental validation is extensive and it demonstrates the improvements introduced by the current approach", "weaknesses": "- Some aspects need to be explained more in detail", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the provided comments. In the following we respond to the raised questions (indicated with Q1, Q2, and Q3).\n\n**Q1 - Class-Incremental with Repetition**\n\nTo clarify, in our approach, the tasks $D_i$ and $ D_j$ are indeed considered disjoint. We acknowledge that our current description could be interpreted as suggesting otherwise, which is not our intention. We will amend the paper to explicitly state that tasks $D_i$ and $D_j$ are mutually exclusive within the same domain $ Y $. The ‘Class-Incremental with Repetition’ setting represents a different scenario and could potentially simplify the problem. We recognize this as an interesting direction for future testing of our SER method (and we will include it in our paper), but our current focus remains on the disjoint class-incremental setting, which poses a more challenging problem.\n\n**Q2 - Clarification to Figure 4**\n\nWe applied the Projected Gradient Descent attack ( [38] of the manuscript) to the ER-ACE + SER model, referred to as SER-ER-ACE and represented by the orange line (the label SAM-ER-ACE was a typo and will be corrected in the final manuscript) on the Split Mini-ImageNet dataset. We compared its performance against the baseline ER-ACE (green line). The figure shows the accuracy drop (in \\%) compared to the standard training performance (values reported in Table 1 of the manuscript) as the attack intensity $\\epsilon$ increases. It is evident that the model equipped with SER experiences a significantly smaller drop and is better able to tolerate this attack.\n\n**Q3 - Clarification to Table 3**\n\nThe values presented in Table 3 of the manuscript correspond to our custom benchmark using MiniImageNet. Specifically, we crafted this benchmark to evaluate the robustness of the SER strategy against spurious features and adversarial attacks. In this benchmark, we selected the first ten classes from MiniImageNet, organized into 5 tasks with 2 classes per task, and introduced spurious features by modifying the brightness of the training images with a class-dependent offset. The test images remained unaltered.\n\nRegarding the reviewer’s concern about the requirement for the saliency encoder and classifier to have the same architecture, we would like to clarify that this does not compromise the model-agnostic nature of our approach. The saliency predictor in the SER strategy does not need to be an external model, but it can be built by creating an additional instance of the classifier, attaching a decoder, and jointly training it with the classifier. Thus, the architecture alignment is not a limitation, as the saliency predictor can be directly constructed using the continually trained classifier.", "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": "3rZX4LZKGX", "reviewer": "Reviewer_oLZW", "summary": "Inspired by some neurophysiological evidence, the author proposed a novel method for online continual learning dubbed as SER, which utilizes visual saliency to modulate the classification network so as to alleviate catastrophic forgetting.\n\nSpecifically, the network architecture follows a dual-branch design where a saliency prediction network and a classification network are trained collaboratively during continual learning.\n\nThe saliency-driven modulation is simply implemented by an element-wise multiplication of intermediate outputs from two branches.\n\nThe comparison experiments demonstrate that the proposed SER module could achieve SOTA results when combined with existing methods.  \n\nAnd the ablation studies further investigate other modulation strategies and demonstrate the robustness of SER against spurious features and adversarial attacks.", "questions": "Based on weaknesses of the manuscript, my questions are listed below:\n\nQ1. What is major difference in novelty, motivation and contribution with prior efforts which also utilizes saliency in continual learning?\n\nQ2. Is there any evidence that SER is still effective when modifying backbone to architectures other than ResNet-18?", "limitations": "As is suggested by the author, one limitation is that saliency encoder has to maintain a homogeneous structure with the classification network, which may potentially hinder its real-world application.", "rating": 7, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "(1) The manuscript is well-organised and the writing is good, making the proposed method easy to understand.\n\n(2) The proposed SER method is simple in design but effective under various OCL settings.", "weaknesses": "(1) The novelty is incremental. A prior work[1] has introduced saliency into class incremental learning, which also shares a similar motivation that low-level visual saliency are stable during continual leanring which could help reduce forgetting.\n\n(2) The claim that “SER is model-agnostic and can be used in combination to any continual learning method” (page 2, line 70) may not be suitable. SER in current version is not model-agnostic since the saliency encoder has to maintain a homogeneous structure with the classification network. In addition, SER may not be compatible with some methods like L2P [2] so it’s not appropriate to claim it could be combined with ‘any’ continual learning method.\n\n(3) some typos should be corrected. One page 8, line 306, the punctuation between ‘training data’ and ‘but’ should be a ‘,’, not a ’.’. In the appendix, On page 16, line 566 and 567, a right bracket ‘$)$’ was missing. \n\nReferences\n\n[1] Liu X, Zhai J T, Bagdanov A D, et al. Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental Learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 23954-23963.\n\n[2] Wang Z, Zhang Z, Lee C Y, et al. Learning to prompt for continual learning[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 139-149.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their insight. We first address the major weaknesses (indicated with W1 and W2) identified by the reviewer and then respond, point by point, to their raised questions (indicated with Q1 and Q2). We will also review the whole paper to correct the identified typos.\n\n**W1 - Novelty**\n\nWe acknowledge that prior work, specifically Liu et al. (2024), has introduced the use of saliency for exemplar-free class incremental learning. However, as mentioned in our paper (lines 96-105), **Liu et al. (2024)** employ a static, pre-trained saliency detector, thus they **do not demonstrate** (hence do not use) the **forgetting-free capabilities of saliency prediction**, since it is not continuously trained. In contrast, our method continuously trains the visual saliency network, which reduces forgetting by adapting classification features to new data. Furthermore, SER provides a more flexible and generalizable saliency-classification paradigm that adapts to any dataset without external dependencies, as opposed to Liu et al. (2024), which, instead, requires the use of a pre-trained saliency detector trained on the same data distribution as the target data. We believe these enhancements represent significant contribution to ongoing research in class-incremental learning. Finally, **Liu et al. (2024)** is not suitable for online continual learning because it is trained for a large number of epochs (100) and uses 50% of the classes in the first task, distributing the remaining classes across subsequent tasks. In our online continual learning setting, where 100 classes are divided into 20 tasks, we found that it achieved very low performance ( around 6%) as reported in Tab. R1 in the pdf attached to the global rebuttal.\n\n**W2 - Model agnostic**\n\nIt is correct that SER requires the saliency encoder to share the same architecture as the classifier. However, we want to clarify that this requirement does not compromise the model-agnostic nature of our approach. Unlike methods that depend on pre-existing saliency predictors (such as the work by Liu mentioned by the reviewer), SER does not necessitate frozen saliency models as it trains saliency prediction continuously alongside the classifier. This integrated learning strategy involves instantiating an additional instance of the classifier, attaching a decoder, and training the entire system (classifier and saliency predictor) jointly. This can be done given that forgetting-free nature of saliency prediction. This learning design ensures that SER remains adaptable to various model architectures, preserving its model-agnostic characteristic while providing an effective solution for simultaneous saliency prediction and classification.\n\nAs for the compatibility of SER with various continual learning methods, the reviewer correctly points out that SER may not align well with some methods, like L2P, and this is a crucial distinction to address. Prompt learning approaches, such as L2P, operate under a different paradigm where the encoder remains static during continual learning. These methods leverage a pre-trained encoder's global knowledge of the visual world, using learnable prompts to identify the relevant sections of the huge latent space that address specific tasks. This is fundamentally different from the approach taken by SER. SER is tailored for scenarios where models undergo continuous training and learning from scratch. SER design ensures that classification features are dynamically modulated by saliency features throughout the learning process. Such dynamic modulation is essential for the model to adapt continually to new tasks and visual stimuli, which is critical for maintaining high performance and robustness in ever-changing environments. Training from scratch allows the model to develop hierarchical feature representations from the ground up, closely mimicking the visual cortex's processing mechanisms.\n\nThus, while the reviewer is correct that SER cannot be applied to every continual learning method, it is indeed well-suited for models which are trained from scratch. Accordingly, we will reformulate the claims about the applicability of SER to every continual learning method.\n\n**Response to Questions:**\n\n**Q1 - What is the novelty?**\n\nPlease refer to our response to W1.\n\n**Q2 - Generalization of SER to architectures other than ResNet-18**\n\nWe extended our experiments to include additional backbones such as ResNet50, MobileNet V2, and DenseNet-121 (see Table R2 of the attached pdf). In all cases, SER improves the performance of the baseline, whether the classifier C is trained from scratch or with the same weights as the encoder of the saliency predictor S (we replicate the same setting as in Table 1 of our original manuscript). This demonstrates that SER remains effective across different architectures.", "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:
We appreciate the feedback from all reviewers and provide an overview of our responses to the major concerns raised. Detailed responses are addressed individually for each reviewer.

**Reviewer U9b5**

**1. Activation Maps per Layer (W1):**
We have investigated per-layer activation maps using GradCAM and LayerIntegratedGradients. Our results, illustrated respectively in Figure R1 and Figure R2 of the attached PDF, show degradation in activation maps across layers as the model trains, indicating widespread catastrophic forgetting.

**2. Comparison with Recent OCL Methods (W2):**
We have extended our comparison to include recent methods like PEC ([R1]) and OnPro ([R13]) within the Mammoth framework (Table R1 of the attached PDF). Results show that methods trained with the SER strategy outperform both their counterparts without SER (as already presented in the paper Table 1) and existing methods by several percentage points, validating the effectiveness of our approach also compared to recent OCL strategies.

**Reviewer L6ph**

**1. Generalization to Other Models (W1):**
We extended experiments to ResNet-50, MobileNet, and DenseNet, showing that SER improves performance across multiple architectures. The exclusion of Vision Transformers (ViT) is due to its poor performance in online continual learning scenarios, when trained from scratch, where it achieved only chance-level accuracy.

**2. Generalization to Transformer Models (W2):**
SER is designed to emulate visual cortex mechanisms, aligning with CNNs due to their hierarchical processing, supported by empirical studies. Vision Transformers, though very effective, do not fit our model's objectives due to their different processing characteristics and data needs. CNNs are more suitable for our goals of hierarchical, localized processing.

**3. More Detailed Description of the Method (W3):**
We acknowledge the request for a more detailed description and will provide additional details in the revised manuscript.

**Reviewer H211**

**1. Class-Incremental with Repetition (Q1):**
We clarify that tasks $D_i$ and $D_j$ are disjoint and will update the paper to reflect this explicitly. The 'Class-Incremental with Repetition' scenario is an interesting direction for future testing of SER, but is outside the scope of our current study that, instead, targets a more challenging setting.

**2. Clarification to Figure 4 and Table 3 (Q2 and Q3):**
Figure 4 shows the performance of ER-ACE+SER on the Split Mini-ImageNet dataset to adversarial attacks. Table 3, instead, shows the robustness of ER-ACE+SER to spurious features using a custom benchmark, constructed in a way to enforce the presence of these spurious features.

**3. Saliency Encoder and Classifier Architecture (Q4):**
The alignment of the saliency encoder with the classifier architecture does not compromise the model-agnostic nature of SER. The saliency predictor can be built as an instance of the classifier with an added decoder, allowing continuous training and adaptability of the two paired networks.

**Reviewer oLZW**

**1. Novelty (W1):**
While Liu et al. (2024) use a static saliency detector, our method continuously trains the saliency network, which reduces forgetting and adapts to new data. SER's flexibility and generalizability, without reliance on pre-trained detectors, represent significant contributions. Moreover, Liu et al.’s method (TASS), trained for multiple epochs, did not perform well on our benchmarks (see Table R1 in the attached pdf).

**2. Model Agnostic (W2):**
SER requires the saliency encoder to share the same architecture as the classifier, but this does not compromise its model-agnostic nature (please refer to our above response to Q4 by Rev. RH211). Unlike methods that depend on static encoders (e.g., L2P), SER dynamically trains both components, making it adaptable to various model architectures. We will adjust our claims to reflect SER’s applicability to models trained from scratch.

In the following, we report the references for the methods used for comparison in Table R1 of the PDF attached to this rebuttal.

- [R1] Zajac et al. Prediction error-based classification for class-incremental learning. ICLR 2024.
- [R2] Liu et al. Task-adaptive saliency guidance for exemplar-free class incremental learning. CVPR 2024.
- [R3] Riemer et al. Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. ICLR 2019.
- [R4] Chaudhry et al. Efficient Lifelong Learning with A-GEM. ICLRW 2019.
- [R5] Wu et al. Large scale incremental learning. CVPR 2019.
- [R6] Benjamin et al. Measuring and regularizing networks in function space. ICLRW 2019.
- [R7] Lopez-Paz and Ranzato. Gradient episodic memory for continual learning. NIPS 2017.
- [R8] Prabhu et al. GDumb: A simple approach that questions our progress in continual learning. ECCV 2020.
- [R9] Aljundi et al. Gradient Based Sample Selection for Online Continual Learning. NIPS 2019.
- [R10] Rebuffi et al. iCaRL: Incremental classifier and representation learning. CVPR 2017.
- [R11] Hou et al. Learning a unified classifier incrementally via rebalancing. CVPR 2019.
- [R12] Pernici et al. Class-incremental learning with preallocated fixed classifiers. CVPR 2021.
- [R13] Wei et al. Online prototype learning for online continual learning. ICCV 2023.

All references to tables and figures come with the suffix *R* to avoid any confusion with those reported in the manuscript.

Meta-review: This paper proposes a saliency-guided approach to online continual learning. They authors propose to leverage saliency-based features that remain more stable across incremental learning tasks. Reviewer opinions were initially positive, although reviewers raised concerns over missing comparisons with state-of-the-art saliency-based approaches and clarity/thoroughness of technical presentation and descriptions. The authors provided a comprehensive and convincing rebuttal that addressed all major reviewer concerns. The authors are encouraged to incorporate the new comparisons and analyses provided in rebuttal in the final version of this work.
Decision: Accept (spotlight)