Title: Saliency-driven Experience Replay for Continual Learning
Venue: NeurIPS 2024
Accepted: yes
Keywords: Continual Learning

Transfer Learning

Saliency Prediction
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

Saliency-driven Experience Replay for Continual 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 .

1 Introduction

Humans possess the remarkable capability to 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

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 (CLS) 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 a fast learner , 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 V1 (one of the earliest areas involved in vision) are as tangled as the pixel space. In other 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 - is in stark contrast to the biological counterparts observed in primates. 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.

Given these premises, it is peculiar that existing bio-inspired continual learning methods tend to ignore all upstream neural processes underlying visual categorization, such as visual saliency processes. Indeed, the ability to 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 a form of genetic legacy from ancestral experience, and tend to remain stable over time - thus not subject to forgetting, though we have long stopped hunting to survive. Interestingly, we observed the same forgetting-free behavior for saliency prediction on artificial neural networks. Fig. 1 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 a 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.

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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 a 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

visual processing [63, 45], SER adopts a two-branch model: one branch performs visual saliency prediction [37, 27, 20], and its responses modulate the features learned by a paired classification model in the second branch.

While the SER strategy stands out in 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 as 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.

SER is model-agnostic and can be used in combination to any continual learning method. We demonstrate that saliency modulation positively impacts classification performance in online continual learning settings, leading to a significant gain in accuracy (up to 20 percent points) w.r.t. baseline methods. We further demonstrate the usefulness of saliency modulation on different benchmarks (including a challenging one that tackles fine-grained classification) and substantiate our claims through a 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 of spurious features and to adversarial attacks.

SaliencyEncoder

Decoder

Gradient

SaliencyPrediction

Prediction

Ground-Truth

ndui

SER

SER

SER

SER

SER

AttentionModulation

Gradient

ClassifierEncoder

Predictor

Classification

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 to 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 real-world settings occur only once. To address this challenge, many strategies adopt a 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 of the shared latent space, while ER-ACE [11]adjusts 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 a conjugate forgetting-free task (i.e., saliency prediction) to modulate the responses of our OCL model. Doing so produces a stabilizing effect on our model and makes it more resilient to forgetting.

An approach similar in the spirit to ours is [39] that leverages saliency prediction for exemplar-free class incremental learning. To compensate for the absence of past task data, this methods relies on

a 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 a 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-classification paradigm that adapts to any dataset without external dependencies, as opposed to [39], which requires the use of a 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 object-identifying 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-weighted Class Activation Mapping (Grad-CAM) [61], 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-CAM, but it 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).

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 its potential impact on real-world applications.

3 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-driven modulation does not require the presence or knowledge of task boundaries , in this formulation and in our experiments we assume that these are given, to the benefit of any baseline method enhanced by the proposed extension. More formally, let D = {D 1 , . . . , D T } be a sequence of data streams, where each pair ( x , y ) ∼ D i denotes a data point x ∈ X with the corresponding class label y ∈ Y ; the sample distributions (in terms of both the data point and the class label) differ between separate streams D i and D j -the sets of class labels in each stream are disjoint, though both belong to the same domain Y . Given a classifier f : X → Y , parameterized by θ , the objective of OCL is to train f on D , organized as a sequence of T tasks { τ 1 , . . . , τ T } , under the constraint that, at a generic task τ i , the model receives inputs sampled from the corresponding data distribution, i.e., ( x , y ) ∼ D i , 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:

where θ i and M i represent the set of model parameters and the buffer at the end of task τ i , respectively. For methods that do not exploit buffer, M i = ∅ , ∀ i .

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:

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 is grounded on the neurophysiological evidence that attention-driven neuronal firing rate modulation is multiplicative and the scaling of neuronal responses depends on the similarity between a neuron's preferred stimulus and the attended feature [63, 45]. This hypothesis is translated into a 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 a 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 S the space of output saliency maps. Generally, if X = R 3 × H × W for RGB images, then S = R H × W , where each location of a map s ∈ S measures the saliency of the corresponding pixel in the RGB space. We assume that S can be decomposed into two functions, an encoder E : X → H and a decoder D : H → S , such that S ( x ) = D ( E ( x )) , for x ∈ X . Then, given an online continual learning problem with data stream D and set of classes Y , let C : X → Y 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 S 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 an 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 can be either a set of ground-truth maps, when available, or pseudo-labels provided as the output of a pre-trained saliency predictor (unrelated to S ). We therefore proceed to optimize a multi-objective loss function L = L s + λ L c , with λ being a weighing hyperparameter. Loss term L s 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 as a saliency prediction objective [10, 20, 4, 69, 26]):

with s i and S i ( x ) iterating over map pixels in s and S ( x ) , respectively. Loss term L c encodes a generic online continual learning objective, as introduced in Eq. 2. As the proposed approach is method-agnostic, details on the formulation of L c 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 a 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 { l 1 , l 2 , . . . , l L } . Without loss of generality, let each layer l i compute its output as z i = σ ( W i z i -1 ) , with σ being an activation function, W i the network-specific layer parameters (i.e., not shared between E and C ) and z i -1 the output of the previous layer (or the network's input x , if appropriate). Then, let us distinguish between features z ( s ) i and z ( c ) i , respectively representing the output of layer l i by the saliency prediction encoder S and the classifier C . We apply saliency-driven modulation by modifying the computation of z ( c ) i as follows:

where ⊙ denotes the Hadamard product. Intuitively, the proposed approach encourages the classification model to attend to 'salient' features of the input, where the concept of saliency is generalized from the pixel space to hidden representations. It is important to note that, at training time, gradient descent optimization of L c would also affect on the saliency encoder E . This is undesirable, as we

previously showed (see Fig. 1) that saliency features are robust to task shifts, unlike classification features: hence, in order to guarantee this property, we stop the gradient flow from L 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, a feature extractor implemented as a neural network. As such, the proposed method can 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 class-representative prototypes (e.g., CoPE [15]).

4 Performance Analysis

4.1 Experimental setup

Benchmarks. We build two OCL benchmarks by taking image classification datasets and splitting their classes equally into a series of disjoint tasks:

Split Mini-ImageNet [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 1 [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 288 × 384 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:

DER++ [7]: a seminal work that combines rehearsal and knowledge distillation strategies for supporting model plasticity while limiting forgetting.

ER-ACE [11]: a variant of Experience Replay [54, 57] which aims to 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 a model to preserve outputs of past model instances on new samples to limit forgetting.

oEWC [30]: a 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 a 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 s from Eq. 3, with λ = 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 to 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 a workstation with an 24-core

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 1 T ∑ T i =1 a T i , where a T i is the accuracy of the final model on the test set of task τ i . Accuracy a T i can be computed in a Class-Incremental Learning ( Class-IL ) or in a Task-Incremental Learning ( Task-IL ) setting. In 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 a given sample. While Task-IL is 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.

4.2 Results

We first evaluate the contribution that saliency-driven modulation provides to state-of-the-art OCL baselines. For each method, we compute Class-Incremental 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 a dual-backbone architecture to decouple incremental classification (by a fast learner ) from self-supervised representation learning [73] (by a slow learner ). We adapt SER to DualNet by replacing the slow learner and its training objective with our saliency prediction backbone, forcing the fast learner to use saliency features for classification.

TwF [8] employs a frozen pre-trained classification backbone to stabilize the learning of ClassIncremental 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 1, showing a 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 to better representation for classification than, for instance, contrastive learning (as done in DualNet) or feature attention with a pre-trained backbone (as in TwF) 2 . This is inline with cognitive neuroscience [19, 35], for which object identity-preservation, that also involves contrastive learning, happens mostly at later layers (e.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 in the categorization process, in a multiplicative fashion. Our next experiments are meant to assess whether this hypothesis (i.e., feature modulation through multiplication for all classification layers) is optimal also for artificial neural networks, or 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:

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 z ( c ) i -1 and saliency features z ( s ) i -1 (see Eq. 4), we feed them to convolutional layer with 1 × 1 kernel to produce z ( c ) i , and let the model learn the corresponding parameters.

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Fig. 3 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, it is interesting to 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.i.d. with respect to the data stream.

We then investigate whether the impact of selective-driven modulation is uniform across the backbone layers. To this aim, we define a positional binary coding scheme, controlling the application of the SER strategy at the predefined points of the network (see Sect. 4.1): if position i of the coding 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].

4.4 Model Robustness

We finally assess the robustness of 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

(e.g., in a 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 to exploit spurious features to solve classification tasks. We crafted an ad-hoc benchmark consisting of ten classes from ImageNet. For each class, we added a 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 a class-dependent offset, computed as 5( c +1) (in a 0-255 brightness range), where c is a numeric class label. We then define five continual learning tasks with two classes each. We then compare ER-ACE to the corresponding SER-enabled variant and ground its performance with the one obtained when it is trained with original images (i.e., without enforcing spurious features in the data). Results in Table 3 show that SER effectively limits the possibility for the classifier to use spurious features, resulting in a 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, we 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 ε 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.

5 Conclusion

We presented SER, a biologically-inspired saliency-driven modulation strategy for online continual learning, which regularizes classification features using visual saliency, effectively reducing forgetting. The proposed approach, grounded on neurophysiological evidence, significantly improves performance of state-of-the-art OCL methods, and has been shown to be superior to other multibranch solutions, either biologically-inspired (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 as auxiliary knowledge to mitigate forgetting in continual learning. This involves pre-training our saliency predictor with an oracle, which could be in the form of either ground-truth maps or an external model generating pseudo-labels. High-quality input images are necessary for producing meaningful saliency maps, thus, datasets like CIFAR10/100 cannot be employed due to their lower resolution. Although SER is model-agnostic, its formulation necessitates that the saliency encoder and the classifier share identical architectures. To apply this to heterogeneous networks, we 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 to investigating whether other low-level visual tasks share this property.

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A Supplementary Materials

A.1 Architectural Details of the Saliency Prediction Network

[128 × 144 × 192]

Skip-2

Skip-4

Input

[256 × 72 × 96]

Prediction

[128 × 72 × 96]

[64 × 144 × 192]

ResNet18

Encoder

Post-cnn

Upsampling-1

Upsampling-2

Upsampling-2

Post

Fusion

[3 × 288 × 384]

[512 × 36 × 48]

[256 × 36 × 48]

[256 × 72 × 96]

[384 × 72 × 96]

[128 × 144 × 192]

[192 × 144 × 192]

[64 × 144 × 192]

[1 × 288 × 384]

For our experiments we create an ad-hoc encoder-decoder saliency prediction network with skip connections. This network uses a 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 a short runtime if compared to other saliency models 3 . In particular, the decoder consists of a stack of pointwise convolutions and deptwhise separable 3 × 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-1. Essentially, features from the bottleneck are upsampled with a factor α = 2 and concatenated with the output of Skip-2 module. The obtained features maps are upsampled again with a factor β = 2 and concatenated with the output of Skip-4 module, while the number of feature maps is progressively scaled from the original value of 512 to 64. One last 1 × 1 convolution, followed by an upsampling layer and logistic activation, reduces the feature maps to 1 and the spatial sizes are restored to those of the input image. More details are reported in Table ST-1.

A.2 Additional experiments

A.2.1 Additional Comparison with Recent CL methods

We further extend the performance analysis by comparing our SER strategy to other prominent CL methods in the Class-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 1 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 differences between the two approaches. First, TASS employs a static, pre-trained saliency detector, which does not showcase the forgetting-free capabilities of saliency prediction since it is not continuously trained, unlike SER. Additionally, TASS is not designed for the OCL scenario, as it requires a large number of training epochs per task (100) and, in its original implementation, uses 50% of the classes in the first task.

A.2.2 Task-Incremental Learning setting performance

Table ST-3 reports the Task-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-Incremental and Task-Incremental 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-Incremental; a marginal gain in Class-Incremental is also noticeable, though the low performance of the baseline methods limits the room for improvements.

A.2.4 Effect of classification pre-training

In Table 1 of the paper we have reported the results of our experiments when the classification backbones of the baseline methods are initialized to the same weights as the saliency encoder, for a fair comparison. In this section, in 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 on a classification pre-text task (despite using classification-pretrained features appears to be in contrast to what it happens in the human brain). Differently from what described in 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

pre-training) and have reached their maximum capacity. These results confirm again the contribution of the forgetting-free behaviour of the saliency prediction task to classification tasks.

A.2.5 Backbones Comparison

We performed other experiments including alternative backbones beyond the classical ResNet-18 to 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

CC

KLD

0.80

0.20

0.86

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0.18

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0.16

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0.65

0.14

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0.12

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0.10

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0.08

0.78

0.45

0.06

71

T5

T10

T15

T20

T5

T10

T15

T20

T1

T5

710

T15

T20

We here report quantitative performance of estimated saliency in CL settings. Fig SF-2, in particular, shows the forgetting-free behaviour of saliency predictions: Pearson's Correlation Coefficient (CC), Similarity (SIM) and Kullback-Leibler divergence (KLD) (metrics commonly employed for saliency predictions [10] do not degrade with the number of CL tasks.

A.2.7 Cost Analysis

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 to note that in a continual learning settings, efficiency at training times might be more relevant than the one at inference times as the main assumption is of a deep model that keeps training from an 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 ∼ 10 points).

Additionally, in 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 a negligible impact on the training time.

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.

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 in our experiments during the continual training. Each name corresponds to a different synset of the ImageNet dataset.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nInspired 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.\n\nSTRENGTHS:\n(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.\n\nWEAKNESSES:\n(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.\n\nQUESTIONS:\nBased 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?\n\nLIMITATIONS:\nAs 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.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn 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.\n\nSTRENGTHS:\n- 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\n\nWEAKNESSES:\n- Some aspects need to be explained more in detail\n\nQUESTIONS:\nHere 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?\n\nLIMITATIONS:\nOne 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.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\n1) 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.\n\nWEAKNESSES:\n1) 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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS:\n1. 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.\n\nWEAKNESSES:\n1. 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.\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nAddressed in paper.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}