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Found 4 papers, 0 papers with code
S-JEA: Stacked Joint Embedding Architectures for Self-Supervised Visual Representation Learning
In this work, we aim to learn highly separable semantic hierarchical representations by stacking Joint Embedding Architectures (JEA) where higher-level JEAs are input with representations of lower-level JEA.
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes
Hyperbolic manifolds for visual representation learning allow for effective learning of semantic class hierarchies by naturally embedding tree-like structures with low distortion within a low-dimensional representation space.
Hyperspherically Regularized Networks for Self-Supervision
Bootstrap Your Own Latent (BYOL) introduced an approach to self-supervised learning avoiding the contrastive paradigm and subsequently removing the computational burden of negative sampling associated with such methods.
The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector
Data sharing remains a major hindering factor when it comes to adopting emerging AI technologies in general, but particularly in the agri-food sector.
