Performative Drift Resistant Classification Using Generative Domain Adversarial Networks
This addresses a specific challenge in machine learning for scenarios where retraining is infeasible, though it appears incremental as it builds on existing adversarial network methods.
The paper tackles the problem of performative drift, where model predictions affect future data, by proposing the Generative Domain Adversarial Network (GDAN) to create drift-resistant classifiers, with initial results showing it limits performance degradation over time.
Performative Drift is a special type of Concept Drift that occurs when a model's predictions influence the future instances the model will encounter. In these settings, retraining is not always feasible. In this work, we instead focus on drift understanding as a method for creating drift-resistant classifiers. To achieve this, we introduce the Generative Domain Adversarial Network (GDAN) which combines both Domain and Generative Adversarial Networks. Using GDAN, domain-invariant representations of incoming data are created and a generative network is used to reverse the effects of performative drift. Using semi-real and synthetic data generators, we empirically evaluate GDAN's ability to provide drift-resistant classification. Initial results are promising with GDAN limiting performance degradation over several timesteps. Additionally, GDAN's generative network can be used in tandem with other models to limit their performance degradation in the presence of performative drift. Lastly, we highlight the relationship between model retraining and the unpredictability of performative drift, providing deeper insights into the challenges faced when using traditional Concept Drift mitigation strategies in the performative setting.