Learning the importance of training data under concept drift (blog.research.google)

Posted by Nishant Jain, Pre-doctoral Researcher, and Pradeep Shenoy, Research Scientist, Google Research The constantly changing nature of the world around us poses a significant challenge for the development of AI models. Often, models are trained on longitudinal data with the hope that the training data used will accurately represent inputs the model may receive in the future. More generally, the default assumption that all training data are equally relevant often breaks in practice. For example, the figure below shows images from the CLEAR nonstationary learning benchmark, and it illustrates how visual features of objects evolve significantly over a 10 year span (a phenomenon we refer to as slow concept drift ), posing a challenge for object categorization models. Sample images from the CLEAR benchmark. (Adapted from Lin et al . ) Alternative approaches, such as online and continual learning , repeatedly update a model with small amounts of recent data in order to keep it current. This implicitly prioritizes recent data, as the learnings from past data are gradually erased by subsequent updates. However in the real world, different kinds of information lose relevance at different rates, so there are two key issues: 1) By design they focus exclusively on the most recent data and lose any signal from older data that is erased. 2) Contributions from data instances decay uniformly over time irrespective of the contents of the data. In our recent work, “ Instance-Conditional Timescales of Decay for Non-Stationary Learning ”, we propose to assign each instance an importance score during training in order to maximize model performance on future data. To accomplish this, we employ an auxiliary model that produces these scores using the training instance as well as its age. This model is jointly learned with the primary model. We address both the above challenges and achieve significant gains over other robust learning methods on a range of benchmark datasets for nonstationary learning. For instance, on a recent large-scale benchmark for nonstationary learning (~39M photos over a 10 year period), we show up to 15% relative accuracy gains through learned reweighting of training data. The challenge of concept drift for supervised learning To gain quantitative insight into slow concept drift, we built classifiers on a recent photo categorization task , comprising roughly 39M photographs sourced from social media websites over a 10 year period. We compared offline training, which iterated over all the training data multiple times in random order, and continual training, which iterated multiple times over each month of data in sequential (temporal) order. We measured model accuracy both during the training period and during a subsequent period where both models were frozen, i.e., not updated further on new data (shown below). At the end of the training period (left panel, x-axis = 0), both approaches have seen the same amount of data, but show a large perfo