Self-Supervised Representation Learning via Neighborhood-Relational Encoding

by   Mohammad Sabokrou, et al.

In this paper, we propose a novel self-supervised representation learning by taking advantage of a neighborhood-relational encoding (NRE) among the training data. Conventional unsupervised learning methods only focused on training deep networks to understand the primitive characteristics of the visual data, mainly to be able to reconstruct the data from a latent space. They often neglected the relation among the samples, which can serve as an important metric for self-supervision. Different from the previous work, NRE aims at preserving the local neighborhood structure on the data manifold. Therefore, it is less sensitive to outliers. We integrate our NRE component with an encoder-decoder structure for learning to represent samples considering their local neighborhood information. Such discriminative and unsupervised representation learning scheme is adaptable to different computer vision tasks due to its independence from intense annotation requirements. We evaluate our proposed method for different tasks, including classification, detection, and segmentation based on the learned latent representations. In addition, we adopt the auto-encoding capability of our proposed method for applications like defense against adversarial example attacks and video anomaly detection. Results confirm the performance of our method is better or at least comparable with the state-of-the-art for each specific application, but with a generic and self-supervised approach.



There are no comments yet.


page 1

page 5

page 7


Using Multiple Self-Supervised Tasks Improves Model Robustness

Deep networks achieve state-of-the-art performance on computer vision ta...

Revisiting Self-Supervised Visual Representation Learning

Unsupervised visual representation learning remains a largely unsolved p...

Discriminative-Generative Representation Learning for One-Class Anomaly Detection

As a kind of generative self-supervised learning methods, generative adv...

Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning

Instance discriminative self-supervised representation learning has been...

Self-supervised Graph-level Representation Learning with Local and Global Structure

This paper studies unsupervised/self-supervised whole-graph representati...

AVID: Adversarial Visual Irregularity Detection

Real-time detection of irregularities in visual data is very invaluable ...

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

For personalized medicines, very crucial intrinsic information is presen...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.