Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics (2007.07400v1)

Abstract: A central challenge in developing versatile machine learning systems is catastrophic forgetting: a model trained on tasks in sequence will suffer significant performance drops on earlier tasks. Despite the ubiquity of catastrophic forgetting, there is limited understanding of the underlying process and its causes. In this paper, we address this important knowledge gap, investigating how forgetting affects representations in neural network models. Through representational analysis techniques, we find that deeper layers are disproportionately the source of forgetting. Supporting this, a study of methods to mitigate forgetting illustrates that they act to stabilize deeper layers. These insights enable the development of an analytic argument and empirical picture relating the degree of forgetting to representational similarity between tasks. Consistent with this picture, we observe maximal forgetting occurs for task sequences with intermediate similarity. We perform empirical studies on the standard split CIFAR-10 setup and also introduce a novel CIFAR-100 based task approximating realistic input distribution shift.

Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics

The paper of catastrophic forgetting in neural networks remains critical in advancing machine learning systems capable of sequential task learning. While various methods have been proposed to mitigate this challenge, understanding the fundamental properties and mechanics behind catastrophic forgetting is imperative for developing more effective solutions. The paper "Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics" presents an in-depth examination of how catastrophic forgetting affects hidden representations and task semantics in neural networks and provides insights into the layers most affected during sequential training.

Core Contributions and Findings

The paper identifies several key points related to how catastrophic forgetting impacts neural networks:

1. Layer-Specific Effects: An empirical investigation using representational similarity measures reveals that deeper layers are disproportionately the source of forgetting. Techniques such as layer freezing and layer resetting experiments show that lower layers maintain stability, whereas deeper layers undergo significant changes during sequential training. This finding is consistent across various architectures and datasets, including the split CIFAR-10 task and a novel CIFAR-100 distribution shift task.
2. Mitigating Forgetting: The paper evaluates popular mitigation strategies such as elastic weight consolidation (EWC) and replay buffers. Both methods function by stabilizing deeper representations, as evidenced by improvements in representational similarity measures correlating with the strength of the forgetting mitigation approach.
3. Semantic Influence: The paper explores how semantic similarities between sequential tasks influence forgetting. In distinct setups, it is observed that tasks with intermediate semantic similarity result in maximal forgetting. This conclusion is supported by empirical tasks and formalized through a simple analytic framework demonstrating that forgetting is most severe for tasks of intermediate similarity.
4. Impact of Selective Feature Representation: Another intriguing observation is that changes in representation can occur without stabilizing weights entirely, suggesting alternative approaches that leverage orthogonalizing representations could mitigate forgetting without the need for conventional stabilization techniques.

Implications and Future Directions

The implications of this research extend to both practical applications and theoretical frameworks for dealing with non-stationary data distributions in machine learning systems:

• Practical Mitigation Strategies: The insights regarding deeper layer stabilization suggest focused development on architectural adaptations or specialized training techniques that target specific layers. This could include maintaining stable representations in later layers, deploying adaptive learning rates, or introducing architectural changes that inherently resist forgetting.
• Handling Input Distribution Shift: The introduction of the CIFAR-100 distribution shift task appropriately simulates real-world scenarios reflected in continual learning applications. The mechanisms outlined for dealing with shifting distributions provide valuable guidelines in designing systems capable of seamlessly adapting to dynamic environments.
• Broader Understanding of Task Semantics: By elucidating the semantic relationships that affect forgetting, this paper opens the path for further exploration into task selection and sequence optimization to mitigate catastrophic forgetting effectively. Extending investigations to measure and manipulate task similarity within broader and more complex task ecosystems is a potential avenue for research and advancement.

The paper’s findings highlight the necessity for a nuanced understanding of neural network behavior under sequential training regimes, encouraging the development of more robust models and providing vital theoretical foundations towards achieving seamless learning capabilities. As artificial intelligence systems continue to evolve, addressing catastrophic forgetting and maximizing knowledge retention across multiple tasks will remain a cornerstone of research in machine learning.