Center-wise Local Image Mixture For Contrastive Representation Learning

11/05/2020
by   Hao Li, et al.
2

Recent advances in unsupervised representation learning have experienced remarkable progress, especially with the achievements of contrastive learning, which regards each image as well its augmentations as a separate class, while does not consider the semantic similarity among images. This paper proposes a new kind of data augmentation, named Center-wise Local Image Mixture, to expand the neighborhood space of an image. CLIM encourages both local similarity and global aggregation while pulling similar images. This is achieved by searching local similar samples of an image, and only selecting images that are closer to the corresponding cluster center, which we denote as center-wise local selection. As a result, similar representations are progressively approaching the clusters, while do not break the local similarity. Furthermore, image mixture is used as a smoothing regularization to avoid overconfidence on the selected samples. Besides, we introduce multi-resolution augmentation, which enables the representation to be scale invariant. Integrating the two augmentations produces better feature representation on several unsupervised benchmarks. Notably, we reach 75.5 ResNet-50, and 59.3 well as consistently outperforming supervised pretraining on several downstream transfer tasks.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/04/2020

Hierarchical Semantic Aggregation for Contrastive Representation Learning

Self-supervised learning based on instance discrimination has shown rema...
03/26/2021

Unsupervised Document Embedding via Contrastive Augmentation

We present a contrasting learning approach with data augmentation techni...
11/15/2021

Contrastive Representation Learning with Trainable Augmentation Channel

In contrastive representation learning, data representation is trained s...
04/08/2022

Automatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation Learning

Contrastive learning enables learning useful audio and speech representa...
06/17/2020

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Unsupervised image representations have significantly reduced the gap wi...
08/21/2021

Unsupervised Local Discrimination for Medical Images

Contrastive representation learning is an effective unsupervised method ...
06/17/2020

LSD-C: Linearly Separable Deep Clusters

We present LSD-C, a novel method to identify clusters in an unlabeled da...