Search Results for author:
Found 7 papers, 4 papers with code
TURBO: The Swiss Knife of Auto-Encoders
We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods.
Stochastic Digital Twin for Copy Detection Patterns
Copy detection patterns (CDP) present an efficient technique for product protection against counterfeiting.
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation
We present a new method of self-supervised learning and knowledge distillation based on the multi-views and multi-representations (MV-MR).
Ranked #2 on
Unsupervised Image Classification
on STL-10
Turbo-Sim: a generalised generative model with a physical latent space
We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model.
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
The InfoSCC-GAN architecture is based on an unsupervised contrastive encoder built on the InfoNCE paradigm, an attribute classifier and an EigenGAN generator.
ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasets
In this paper, we consider a problem of self-supervised learning for small-scale datasets based on contrastive loss between multiple views of the data, which demonstrates the state-of-the-art performance in classification task.
Ranked #1 on
Unsupervised Image Classification
on CIFAR-20
