Don't miss the Mismatch: Investigating the Objective Function Mismatch for Unsupervised Representation Learning

TL;DR

This paper investigates the objective function mismatch in unsupervised representation learning, proposing new evaluation metrics to quantify it and analyzing its impact across various tasks and datasets.

Contribution

It introduces general evaluation metrics for objective function mismatch and studies their effects on unsupervised learning performance across multiple datasets.

Findings

Objective function mismatch reduces performance by 0.1-5% on some datasets.

Mismatch can cause performance drops of 25-59% in extreme cases.

The mismatch depends on model size, complexity, and data augmentations.

Abstract

Finding general evaluation metrics for unsupervised representation learning techniques is a challenging open research question, which recently has become more and more necessary due to the increasing interest in unsupervised methods. Even though these methods promise beneficial representation characteristics, most approaches currently suffer from the objective function mismatch. This mismatch states that the performance on a desired target task can decrease when the unsupervised pretext task is learned too long - especially when both tasks are ill-posed. In this work, we build upon the widely used linear evaluation protocol and define new general evaluation metrics to quantitatively capture the objective function mismatch and the more generic metrics mismatch. We discuss the usability and stability of our protocols on a variety of pretext and target tasks and study mismatches in a wide range of experiments. Thereby we disclose dependencies of the objective function mismatch across several pretext and target tasks with respect to the pretext model's representation size, target model complexity, pretext and target augmentations as well as pretext and target task types. In our experiments, we find that the objective function mismatch reduces performance by ~0.1-5.0% for Cifar10, Cifar100 and PCam in many setups, and up to ~25-59% in extreme cases for the 3dshapes dataset.