Can contrastive learning avoid shortcut solutions? (2106.11230v3)

Abstract: The generalization of representations learned via contrastive learning depends crucially on what features of the data are extracted. However, we observe that the contrastive loss does not always sufficiently guide which features are extracted, a behavior that can negatively impact the performance on downstream tasks via "shortcuts", i.e., by inadvertently suppressing important predictive features. We find that feature extraction is influenced by the difficulty of the so-called instance discrimination task (i.e., the task of discriminating pairs of similar points from pairs of dissimilar ones). Although harder pairs improve the representation of some features, the improvement comes at the cost of suppressing previously well represented features. In response, we propose implicit feature modification (IFM), a method for altering positive and negative samples in order to guide contrastive models towards capturing a wider variety of predictive features. Empirically, we observe that IFM reduces feature suppression, and as a result improves performance on vision and medical imaging tasks. The code is available at: \url{https://github.com/joshr17/IFM}.

A Critical Examination of Feature Suppression in Contrastive Learning

In “Can contrastive learning avoid shortcut solutions?”, the authors Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, and Suvrit Sra explore the phenomenon of feature suppression within contrastive learning frameworks and propose an innovative method to mitigate its effects.

Background on Contrastive Learning

Contrastive learning has emerged as a powerful technique for unsupervised representation learning, especially in computer vision tasks. By utilizing mechanisms that drive encoders to discriminate between similar and dissimilar data pairs, contrastive learning focuses on optimizing a loss function known as the InfoNCE loss. However, the clarity and efficacy of learned features can be significantly limited when models exploit "shortcuts" during feature extraction. Such shortcuts often lead to unintentional suppression of features that are predictive or relevant, potentially compromising the transferability and generalization capability of the learned models.

Investigation into Feature Suppression

The paper explores the underlying reasons for feature suppression in contrastive learning, guided by theoretical and empirical assessments. The authors demonstrate that optimizing the InfoNCE loss does not inherently prevent feature suppression, a revelation supported by Proposition 1, which underscores the loss’s potential to achieve optima both when suppressing and distinguishing features. Further analysis reveals a counterintuitive observation—lower InfoNCE loss values do not invariably correlate with improved performance on downstream tasks, owing to shortcuts that exploit simpler features during instance discrimination.

Controlling Feature Learning

To address feature suppression, the authors scrutinize the impact of instance discrimination's difficulty. They test variations in the temperature parameter in the InfoNCE loss and hardness concentration parameters in negative sampling techniques, concluding that leveraging more challenging positive and negative example pairs can prompt variations in learned features. However, this strategy often results in sacrificing the quality of one feature’s representation for another—a perpetual trade-off situation.

Proposition 2 sheds light on the relationship between instance discrimination's difficulty and feature representation propensity: when specific features are consistent across positive and negative pairs, those features are not sufficiently discriminated against, suggesting alternative features be used.

Implicit Feature Modification: Mitigating Feature Suppression

To reduce feature suppression without sacrificing representation quality, the authors propose Implicit Feature Modification (IFM). This approach introduces novel perturbations in the latent space after data encoding rather than directly modifying raw input data, which is relatively less feasible for semantic alterations. IFM systematically guides encoders to learn and represent varied features effectively, ensuring avoidance of consistently relied-on shortcut solutions. Analytical formulations for perturbations derive from effective gradients in the latent space, allowing seamless integration of IFM without computational overhead.

Empirical Validation

Through a series of experimental evaluations across diverse datasets, including image-based and medical imaging tasks, IFM consistently enhances the representation learning capabilities of contrastive models. The paper demonstrates improvements in model accuracy and robustness by facilitating a balanced representation of multiple predictive features.

Implications for AI and Future Directions

The insights in this paper resonate with the broader AI research community’s interests in improving unsupervised representation learning. Mitigating feature suppression is crucial for the robust application of models across varied tasks and domains. IFM exemplifies a promising route to enriching feature extraction in latent spaces, stimulating further research into adversarial techniques and integrations with other contrastive learning frameworks. Future works might investigate IFM’s applicability alongside adversarial robustness strategies or explore its potential in natural language processing tasks where feature richness is paramount.

In conclusion, the authors have contributed significant advancements in understanding the internal biases of contrastive learning and proposed practical methodologies for ameliorating feature suppression problems. Their findings are likely to inform the ongoing development of more adaptable, generalizable AI models.