In the Wild: From ML Models to Pragmatic ML Systems

Enabling robust intelligence in the wild entails learning systems that offer uninterrupted inference while affording sustained learning from varying amounts of data and supervision. The machine learning community has organically broken down this challenging task into manageable sub tasks such as supervised, few-shot, continual, and self-supervised learning; each affording distinct challenges and a unique set of methods. Notwithstanding this remarkable progress, the simplified and isolated nature of these experimental setups has resulted in methods that excel in their specific settings, but struggle to generalize beyond them. To foster research towards more general ML systems, we present a new learning and evaluation framework - In The Wild (NED). NED naturally integrates the objectives of previous frameworks while removing many of the overly strong assumptions such as predefined training and test phases, sufficient amounts of labeled data for every class, and the closed-world assumption. In NED, a learner faces a stream of data and must make sequential predictions while choosing how to update itself, adapt quickly to novel classes, and deal with changing data distributions; while optimizing for the total amount of compute. We present novel insights from NED that contradict the findings of less realistic or smaller-scale experiments which emphasizes the need to move towards more pragmatic setups. For example, we show that meta-training causes larger networks to overfit in a way that supervised training does not, few-shot methods break down outside of their narrow experimental setting, and self-supervised method MoCo performs significantly worse when the downstream task contains new and old classes. Additionally, we present two new methods (Exemplar Tuning and Minimum Distance Thresholding) that significantly outperform all other methods evaluated in NED.