Fortuitous Forgetting in Connectionist Networks (2202.00155v1)

Abstract: Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact be favorable to learning. We introduce "forget-and-relearn" as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting step selectively removes undesirable information from the model, and the relearning step reinforces features that are consistently useful under different conditions. The forget-and-relearn framework unifies many existing iterative training algorithms in the image classification and language emergence literature, and allows us to understand the success of these algorithms in terms of the disproportionate forgetting of undesirable information. We leverage this understanding to improve upon existing algorithms by designing more targeted forgetting operations. Insights from our analysis provide a coherent view on the dynamics of iterative training in neural networks and offer a clear path towards performance improvements.

• The paper demonstrates that targeted forgetting using methods like NWR and IMP enhances generalization by eliminating less informative features.
• The paper outlines a robust methodology where perturbation techniques in image classification and language emergence yield measurable performance gains.
• The paper underscores the potential of purposeful forgetting to reduce overfitting and computational delays, paving the way for more efficient AI training protocols.

Fortuitous Forgetting in Connectionist Networks: A Critical Review

The concept of forgetting in human cognition has traditionally been perceived negatively, akin to a failure of memory. However, in the paper "Fortuitous Forgetting in Connectionist Networks," the authors propose an alternative viewpoint that posits forgetting as a pivotal factor in enhancing learning processes within neural networks. This paper explores the paradigm of "forget-and-relearn," which redefines forgetting not as a downfall but as an advantageous step in training machine learning models.

The Forget-and-Relearn Framework

The paper introduces what the authors term the "forget-and-relearn" hypothesis. According to this concept, a targeted model forgetting phase can strategically remove information assumed to be undesirable. This step is followed by a relearning phase, where only consistently valuable features are reinforced. The paradigm unifies various iterative training methodologies seen in tasks like image classification and language emergence by explaining them through the lens of this disproportionate forgetting of less useful data.

Numerical Insights and Theoretical Constructs

The paper presents compelling quantitative evidence supporting its claims. For instance, in image classification tasks, the authors demonstrate that perturbation techniques such as Network Weight Reinitialization (NWR) and Iterative Magnitude Pruning (IMP) disproportionately impact less informative features. Furthermore, empirical results reveal that, across several datasets, these targeted forgetting methods lead to better generalization by effectively diminishing the focus on memorized or noise-laden features.

The authors apply similar methodologies to language emergence experiments, using a multi-agent Lewis game framework. They illustrate how perturbation facilitates compositional language generation through a forgetting operation tailored to filter out non-compositional language structures. This approach not only validates the proposed hypothesis but also aligns with the theoretical understanding of symbiotic learning from cognitive science.

Implications and Future Directions

The practical implications of targeted forgetting are significant. For instance, in image classification, removing complexity associated with overfitting by selectively erasing intricately memorized patterns results in simpler, more adaptable models. The potential for reducing computational delays, minimizing data overfitting, and tailoring learning mechanisms is immense.

The paper speculatively opens doors to new explorations in AI, suggesting this method could be used in evolving training protocols for better efficiency in data utilization. Further inquiry could revolve around the calibration of forgetting operations, aligning them better with specific datasets or algorithms, and exploring integration with self-regulatory AI systems.

Conclusion

In conclusion, the authors of this paper articulate a comprehensive argument for re-evaluating the negative connotations of forgetting in neural networks. By framing forgetting as a purposeful, structured phase within an iterative training framework, they uncover avenues to enhance model accuracy and generality. This research holds substantial value for ongoing AI research, pointing towards iterative training techniques with intentional forgetting as a means to achieve robust learning architectures optimized for real-world applications. Through forget-and-relearn, the paper elucidates a significant interplay between loss of information and learning, suggesting an innovative path forward for connectionist models.