REMIND Your Neural Network to Prevent Catastrophic Forgetting

Abstract: People learn throughout life. However, incrementally updating conventional neural networks leads to catastrophic forgetting. A common remedy is replay, which is inspired by how the brain consolidates memory. Replay involves fine-tuning a network on a mixture of new and old instances. While there is neuroscientific evidence that the brain replays compressed memories, existing methods for convolutional networks replay raw images. Here, we propose REMIND, a brain-inspired approach that enables efficient replay with compressed representations. REMIND is trained in an online manner, meaning it learns one example at a time, which is closer to how humans learn. Under the same constraints, REMIND outperforms other methods for incremental class learning on the ImageNet ILSVRC-2012 dataset. We probe REMIND's robustness to data ordering schemes known to induce catastrophic forgetting. We demonstrate REMIND's generality by pioneering online learning for Visual Question Answering (VQA).

• The paper demonstrates that REMIND leverages Product Quantization to compress high-dimensional features, effectively mitigating catastrophic forgetting in incremental learning.
• It introduces a biologically inspired memory replay mechanism that optimizes storage by saving compressed representations instead of raw pixel data.
• Empirical results show REMIND outperforms methods like iCaRL on datasets such as ImageNet, paving the way for practical on-device and real-time learning applications.

An Overview of REMIND: A Novel Approach to Preventing Catastrophic Forgetting in Neural Networks

In the academic exploration of artificial neural networks, the challenge of catastrophic forgetting during incremental learning tasks has become prevalent. Neural networks tend to erase old knowledge when trained on new data, limiting their applicability in contexts requiring continuous learning. The paper "REMIND Your Neural Network to Prevent Catastrophic Forgetting" proposes a novel methodology named REMIND, which is inspired by the biological process of memory replay in the brain.

Core Contributions and Methodology

REMIND, which stands for Replay using Memory Indexing, provides a new approach by focusing on storing compressed representations of data rather than raw pixel data, which is the usual practice in traditional replay methods. This concept aligns with hippocampal indexing theory, where the brain consolidates memories through reactivation of compressed representations, rather than verbose reconstructive episodes.

The key technical contribution of REMIND lies in its use of Product Quantization (PQ) to compress the high-dimensional features extracted from convolutional neural networks (CNNs). By storing these representations, REMIND effectively uses available memory resources, allowing it to store significantly more instances compared to models like iCaRL, which store raw pixel data.

REMIND operates in a streaming learning setting, processing data samples sequentially and using a balance of new data and replayed compressed representations to update the trainable layers of the network. This approach not only emulates the biological plausibility of hippocampal replay but also maintains the resource efficiency necessary for deploying models on devices with limited computational power.

Results and Implications

Empirical evaluations of REMIND demonstrate a robust performance across several datasets like ImageNet and CORe50. Notably, REMIND outperforms established methods including iCaRL, Unified, and BiC in a streaming learning scenario, which reflects scenarios more realistic to lifelong learning than the batch learning paradigms typically used in research.

The streaming learning evaluations conducted for REMIND show that it is less susceptible to order influences, which often induce catastrophic forgetting. This suggests broader applicability for REMIND in real-time learning applications such as on-device learning in smart technologies and autonomous systems. Furthermore, REMIND provides a sturdy foundation for extending online learning capabilities to tasks beyond image classification, such as Visual Question Answering (VQA), where it also yields superior results.

Future Directions

The adaptability and efficiency of REMIND indicate its potential as a foundational model for further advancements in continuous learning scenarios. Its architecture might be further optimized by integrating neural network co-designs that better leverage the compressive strengths of PQ or by incorporating more nuanced selective replay strategies inspired by cognitive science.

Moreover, investigation into the dynamic nature of quantization can enhance its deployment versatility, particularly in tasks requiring adaptive learning rates or variable task difficulties. By replicating the benefits of biological memory systems, REMIND’s framework could inspire further development in other areas such as scene understanding, language processing, and even recommendation systems where streaming data input is relevant.

In conclusion, REMIND provides a significant step forward in addressing the challenges of catastrophic forgetting, drawing on both neuroscientific inspiration and advanced computational techniques. Its successful integration of compressed replay strategies offers a pathway to more resilient and efficient neural networks, paving the future of continual learning in artificial intelligence.