# SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer Source: [SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:36:51.485Z Authors: Renan A. Rojas-Gomez, Karan Singhal, Ali Etemad, Alex Bijamov, Warren R. Morningstar, Philip Andrew Mansfield Published: 2023-12-02T17:25:30.000Z ## Abstract Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images. This results in distorted augmented samples with compromised semantic information, ultimately impacting downstream performance. To overcome this limitation, we propose SASSL: Style Augmentations for Self Supervised Learning, a novel data augmentation technique based on Neural Style Transfer. SASSL decouples semantic and stylistic attributes in images and applies transformations exclusively to their style while preserving content, generating diverse samples that better retain semantic information. SASSL boosts top-1 image classification accuracy on ImageNet by up to 2 percentage points compared to established self-supervised methods like MoCo, SimCLR, and BYOL, while achieving superior transfer learning performance across various datasets. Because SASSL can be performed asynchronously as part of the data augmentation pipeline, these performance impacts can be obtained with no change in pretraining throughput. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()