SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915002043072512 |
|---|---|
| author | Rojas-Gomez, Renan A. Singhal, Karan Etemad, Ali Bijamov, Alex Morningstar, Warren R. Mansfield, Philip Andrew |
| author_facet | Rojas-Gomez, Renan A. Singhal, Karan Etemad, Ali Bijamov, Alex Morningstar, Warren R. Mansfield, Philip Andrew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_01187 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer Rojas-Gomez, Renan A. Singhal, Karan Etemad, Ali Bijamov, Alex Morningstar, Warren R. Mansfield, Philip Andrew Computer Vision and Pattern Recognition Machine Learning 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. |
| title | SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2312.01187 |