Leveraging Selective Attention for Robust Continual Learning
Grunnleggende konsepter
Selective attention-driven modulation can effectively enhance the performance and robustness of continual learning models by leveraging the forgetting-free behavior of saliency prediction.
Sammendrag
The paper presents SAM, a biologically-inspired selective attention-driven modulation strategy for online continual learning. The key insights are:
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Neurophysiological evidence shows that the primary visual cortex does not directly contribute to object categorization, but rather enforces luminance and contrast robustness. This suggests that training early layers with a visual categorization objective, as done in existing continual learning methods, is in contrast with the biological counterparts observed in primates.
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Recent findings in cognitive neuroscience have shown that the visual attention priorities of human ancestors are still embedded in the modern brain, suggesting that saliency prediction is a forgetting-free task, unlike classification.
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The proposed SAM approach employs a saliency prediction network to modulate the features learned by a paired classification network, through a multiplicative interaction. This emulates the neurophysiological evidence of attention-driven neuronal firing rate modulation.
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Experimental results confirm that SAM effectively enhances the performance (up to 20 percentage points) of state-of-the-art continual learning methods, both in class-incremental and task-incremental settings. SAM also leads to learn features that are more robust to the presence of spurious features and to adversarial attacks.
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Ablation studies show that the proposed multiplicative modulation strategy is superior to alternative ways of integrating saliency information, and that modulating all classification layers is the most effective approach, in line with neuroscience findings.
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Selective Attention-based Modulation for Continual Learning
Statistikk
"Saliency accuracy (measured as similarity [1]) improves as the saliency network is presented with more tasks, while classification accuracy drops."
"Saliency prediction accuracy, measured in terms of Sim, CC and KLD metrics, does not degrade in continual learning settings."
"The drop of performance (about 22 percentage points) observed between training with the original data and training with data biased by spurious features is almost completely recovered when SAM is used."
Sitater
"Neurophysiological studies [8,9] 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 [9]."
"Recent findings in cognitive neuroscience have shown that the visual attention priorities of human hunter-gatherer ancestors are still embedded in the modern brain [11]: humans pay attention faster to animals than to vehicles, although we now see more vehicles than animals."
Dypere Spørsmål
How can the proposed SAM strategy be extended to handle heterogeneous network architectures for the classification and saliency prediction tasks?
The SAM strategy can be extended to handle heterogeneous network architectures by defining or learning a mapping between activations at different network stages. In the context of the SAM strategy, where the saliency encoder and the classifier are architecturally identical, adapting this approach to heterogeneous networks would involve establishing a mechanism to align the features extracted by different network architectures. This alignment could be achieved by introducing a mapping function that transforms the features extracted by the saliency prediction network to be compatible with the features extracted by the classification network. By learning or defining this mapping, the saliency information can be effectively integrated into the classification process, even when the network architectures are heterogeneous.
How can the finding that saliency prediction is i.i.d. with respect to classification distribution shifts be leveraged to improve continual learning in other ways, beyond the proposed modulation approach?
The finding that saliency prediction is i.i.d. with respect to classification distribution shifts can be leveraged to improve continual learning in various ways beyond the proposed modulation approach. One key application is in the development of robust feature extraction methods for continual learning tasks. By leveraging the i.i.d. nature of saliency prediction, researchers can explore the use of saliency-based features as a stable and reliable representation of visual information across different tasks. These features can serve as a consistent and invariant representation that aids in mitigating forgetting and improving generalization in continual learning scenarios. Additionally, the i.i.d. property of saliency prediction can be utilized to guide the selection of informative samples for replay buffers or memory management strategies, enhancing the efficiency and effectiveness of continual learning algorithms.
What other low-level visual tasks, besides saliency prediction, might enjoy the property of being forgetting-free in continual learning settings, and how can they be exploited to enhance the robustness of classification models?
Other low-level visual tasks that might enjoy the property of being forgetting-free in continual learning settings include edge detection, texture recognition, and color segmentation. These tasks involve fundamental visual processing mechanisms that are essential for scene understanding and object recognition. Similar to saliency prediction, these tasks are often considered to be i.i.d. with respect to changes in the input data distribution, making them suitable candidates for enhancing the robustness of classification models in continual learning scenarios. By leveraging the stability and consistency of features extracted from these low-level visual tasks, researchers can develop complementary learning systems that integrate these features into the classification process. This integration can help in preserving important visual cues, reducing the impact of forgetting, and improving the overall performance and generalization capabilities of classification models in continual learning settings.
