The Pitfalls of Simplicity Bias in Neural Networks (2006.07710v2)

Abstract: Several works have proposed Simplicity Bias (SB)---the tendency of standard training procedures such as Stochastic Gradient Descent (SGD) to find simple models---to justify why neural networks generalize well [Arpit et al. 2017, Nakkiran et al. 2019, Soudry et al. 2018]. However, the precise notion of simplicity remains vague. Furthermore, previous settings that use SB to theoretically justify why neural networks generalize well do not simultaneously capture the non-robustness of neural networks---a widely observed phenomenon in practice [Goodfellow et al. 2014, Jo and Bengio 2017]. We attempt to reconcile SB and the superior standard generalization of neural networks with the non-robustness observed in practice by designing datasets that (a) incorporate a precise notion of simplicity, (b) comprise multiple predictive features with varying levels of simplicity, and (c) capture the non-robustness of neural networks trained on real data. Through theory and empirics on these datasets, we make four observations: (i) SB of SGD and variants can be extreme: neural networks can exclusively rely on the simplest feature and remain invariant to all predictive complex features. (ii) The extreme aspect of SB could explain why seemingly benign distribution shifts and small adversarial perturbations significantly degrade model performance. (iii) Contrary to conventional wisdom, SB can also hurt generalization on the same data distribution, as SB persists even when the simplest feature has less predictive power than the more complex features. (iv) Common approaches to improve generalization and robustness---ensembles and adversarial training---can fail in mitigating SB and its pitfalls. Given the role of SB in training neural networks, we hope that the proposed datasets and methods serve as an effective testbed to evaluate novel algorithmic approaches aimed at avoiding the pitfalls of SB.

• The paper reveals that neural networks trained with SGD exhibit extreme Simplicity Bias by favoring the simplest features even when more complex alternatives offer better predictive insights.
• The paper demonstrates that such bias undermines model robustness, making networks particularly sensitive to distribution shifts and minor adversarial perturbations.
• The paper introduces curated datasets and a theoretical framework as benchmarks to guide future algorithmic strategies that mitigate these biases.

The Pitfalls of Simplicity Bias in Neural Networks

In their paper, the authors critically examine the role of Simplicity Bias (SB) in the training of neural networks using Stochastic Gradient Descent (SGD). The phenomenon whereby neural networks favor simpler models has been observed as a factor attempting to explain their capacity to generalize effectively despite being highly capable of fitting random data. However, the precise notion of simplicity in this context is inadequately defined, and the current theoretical frameworks fail to encompass the documented non-robustness of neural networks in applied settings.

Core Contributions and Observations

The authors design datasets that facilitate a rigorous exploration of SB under controlled yet practically relevant conditions. The curated datasets are carefully structured to integrate features exhibiting varying degrees of simplicity, allowing an in-depth analysis of neural networks' reliance on simple versus complex features.

Four considerable insights are presented from theoretical analysis and empirical investigations:

1. Extreme SB: Neural networks trained with SGD demonstrate an extreme reliance on the simplest feature available, to the point of ignoring more complex, yet potentially richer, predictive features.
2. Impact on Robustness: This extreme SB could elucidate the observed sensitivity of models to distribution shifts and minor adversarial perturbations that severely impact their performance.
3. Generalization Challenges: Notably, SB can adversely affect generalization on the original data distribution, especially when the simplest feature does not possess the highest predictive merit among the available features.
4. Limitations of Standard Methods: Common strategies aimed at bolstering generalization and robustness, such as ensemble methods and adversarial training, fall short in mitigating the pitfalls associated with SB.

Through these revelations, the authors posit that SB contributes significantly to key vulnerabilities in neural networks: poor out-of-distribution (OOD) performance, heightened adversarial susceptibility, and suboptimal generalization. They make a compelling argument for the use of their datasets as practical benchmarks for testing new algorithmic strategies designed to curb the detrimental effects of SB.

Theoretical and Empirical Framework

The theoretical grounding is provided through analysis of one-hidden-layer neural networks on a stylized dataset (LSN), which emulates both linear and more complex 3-slab decision boundaries. Their findings conclusively show that, with SGD, the neural networks' parameters adjust in favor of the simpler linear feature over the complex 3-slab feature.

The empirical section covers a range of architectures, optimizers, and configurations to validate the persistence of extreme SB. This includes experiments on structured datasets comprising MNIST and CIFAR-10 images, observing the same latched dependency on simplified features.

Implications and Future Directions

The paper highlights the need for heightened awareness around SB when assessing neural networks' robustness and generalization capabilities. SB’s adverse effects necessitate novel solutions beyond the current toolbox, calling for innovative approaches in both algorithm design and implementation strategies. Such approaches would ideally focus on explicitly rewarding feature learning that balances simplicity with complexity.

From an applied standpoint, these findings could suggest vital adjustments in both model assessment and development pipelines, ensuring models do not inadvertently sacrifice robustness or generalization capacity for simplicity.

In future advancements, exploring dataset designs and new SGD variants that mitigate SB's influence could lead to robust models capable of recognizing and adapting to the underlying complexity of real-world data. Hence, the datasets and analysis framework provided by this paper offer valuable avenues for ongoing research.