Random Field Augmentations for Self-Supervised Representation Learning
Self-supervised representation learning is heavily dependent on data
augmentations to specify the invariances encoded in representations. Previous
work has shown that applying diverse data augmentations is crucial to
downstream performance, but augmentation techniques remain under-explored. In
this work, we propose a new family of local transformations based on Gaussian
random fields to generate image augmentations for self-supervised
representation learning. These transformations generalize the well-established
affine and color transformations (translation, rotation, color jitter, etc.)
and greatly increase the space of augmentations by allowing transformation
parameter values to vary from pixel to pixel. The parameters are treated as
continuous functions of spatial coordinates, and modeled as independent
Gaussian random fields. Empirical results show the effectiveness of the new
transformations for self-supervised representation learning. Specifically, we
achieve a 1.7% top-1 accuracy improvement over baseline on ImageNet downstream
classification, and a 3.6% improvement on out-of-distribution iNaturalist
downstream classification. However, due to the flexibility of the new
transformations, learned representations are sensitive to hyperparameters.
While mild transformations improve representations, we observe that strong
transformations can degrade the structure of an image, indicating that
balancing the diversity and strength of augmentations is important for
improving generalization of learned representations.
654b0a8c3ed9b2c805053199
Philip Andrew Mansfield
654b0a8c3ed9b2c80505319a
Arash Afkanpour
654b0a8c3ed9b2c80505319b
Warren Richard Morningstar
654b0a8c3ed9b2c80505319c
Karan Singhal
claimed_verified
2023-11-08T17:46:28.581Z
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/avatars/7ad4a33503533ddbb54c71b634fda89b.svg
Karan Singhal
user
karan1149
Random Field Augmentations for Self-Supervised Representation Learning
Self-supervised representation learning is heavily dependent on data
augmentations to specify the invariances encoded in representations. Previous
work has shown that applying diverse data augmentations is crucial to
downstream performance, but augmentation techniques remain under-explored. In
this work, we propose a new family of local transformations based on Gaussian
random fields to generate image augmentations for self-supervised
representation learning. These transformations generalize the well-established
affine and color transformations (translation, rotation, color jitter, etc.)
and greatly increase the space of augmentations by allowing transformation
parameter values to vary from pixel to pixel. The parameters are treated as
continuous functions of spatial coordinates, and modeled as independent
Gaussian random fields. Empirical results show the effectiveness of the new
transformations for self-supervised representation learning. Specifically, we
achieve a 1.7% top-1 accuracy improvement over baseline on ImageNet downstream
classification, and a 3.6% improvement on out-of-distribution iNaturalist
downstream classification. However, due to the flexibility of the new
transformations, learned representations are sensitive to hyperparameters.
While mild transformations improve representations, we observe that strong
transformations can degrade the structure of an image, indicating that
balancing the diversity and strength of augmentations is important for
improving generalization of learned representations.
A new family of local transformations using Gaussian random fields for image augmentations in self-supervised learning improves classification accuracy but requires careful tuning of hyperparameters.
self-supervised representation learning data augmentations Gaussian random fields affine transformations color transformations ImageNet iNaturalist top-1 accuracy out-of-distribution classification hyperparameters