Self-supervised Representation Learning via Self-Evolvable Random Projections

Self-supervised representation learning (SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations. While augmentation-based SSRL algorithms push the boundaries of performance in computer vision and natural language processing, they are often not directly applicable to other data modalities, and can conflict with application specific data augmentation constraints. This project aims to propose an SSRL approach that can be applied to any data modality and network architecture because it does not rely on augmentations or masking.

Faculty Supervisor:

Ga Wu

Student:

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Finance and Insurance; Professional, scientific and technical services

University:

Dalhousie University

Program: