Johannes Lehner, Benedikt Alkin, Andreas Fürst, Elisabeth Rumetshofer, Lukas Miklautz, and Sepp Hochreiter

An overview of the proposed architecture.

An overview of the proposed architecture.

Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a sufficient amount of labeled data since their rich features capture not only objects but also less relevant image background. In contrast, Instance Discrimination (ID) methods focus on objects. In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data. To this end, we introduce Masked Autoencoder Contrastive Tuning (MAE-CT), a sequential approach that applies Nearest Neighbor Contrastive Learning (NNCLR) to a pre-trained MAE. MAE-CT tunes the rich features such that they form semantic clusters of objects without using any labels. Applied to large and huge Vision Transformer (ViT) models, MAE-CT matches or excels previous self-supervised methods trained on ImageNet in linear probing, k-NN and low-shot classification accuracy as well as in unsupervised clustering accuracy. Notably, similar results can be achieved without additional image augmentations. While ID methods generally rely on hand-crafted augmentations to avoid shortcut learning, we find that nearest neighbor lookup is sufficient and that this data-driven augmentation effect improves with model size. MAE-CT is compute efficient. For instance, starting from a MAE pre-trained ViT-L/16, MAE-CT increases the ImageNet 1% low-shot accuracy from 67.7% to 72.6%, linear probing accuracy from 76.0% to 80.2% and k-NN accuracy from 60.6% to 79.1% in just five hours using eight A100 GPUs.

arXiv:2304.10520, 2023-04-20.

View paper
IARAI Authors
Dr Sepp Hochreiter
Autoencoder, Contrastive Learning, Instance Discrimination, Masked Image Modeling, Vision Transformer


Imprint | Privacy Policy

Stay in the know with developments at IARAI

We can let you know if there’s any

updates from the Institute.
You can later also tailor your news feed to specific research areas or keywords (Privacy)

Log in with your credentials

Forgot your details?

Create Account