title: Random Field Augmentations for Self-Supervised Representation Learning
summary: 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.
paper_authors._id: 654b0a8c3ed9b2c805053199
paper_authors.name: Philip Andrew Mansfield
paper_authors._id: 654b0a8c3ed9b2c80505319a
paper_authors.name: Arash Afkanpour
paper_authors._id: 654b0a8c3ed9b2c80505319b
paper_authors.name: Warren Richard Morningstar
paper_authors._id: 654b0a8c3ed9b2c80505319c
paper_authors.name: Karan Singhal
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-08T17:46:28.581Z
paper_authors._id: 654bb16a48b474120258371c
paper_authors.avatarUrl: /avatars/7ad4a33503533ddbb54c71b634fda89b.svg
paper_authors.fullname: Karan Singhal
paper_authors.type: user
paper_authors.user: karan1149
paper_title: Random Field Augmentations for Self-Supervised Representation Learning
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: self-supervised representation learning data augmentations Gaussian random fields affine transformations color transformations ImageNet iNaturalist top-1 accuracy out-of-distribution classification hyperparameters