4 citations · 4 across the 5 of their papers we have counts for
5 papers
Grow, Assess, Compress: Adaptive Backbone Scaling for Memory-Efficient Class Incremental Learning
Adrian Garcia-Castañeda, Jon Irureta, Jon Imaz +1
Class Incremental Learning (CIL) poses a fundamental challenge: maintaining a balance between the plasticity required to learn new tasks and the stability needed to prevent catastr…
Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learning
Jon Irureta, Gorka Azkune, Jon Imaz +2
Vertical Federated Learning (VFL) has emerged as a critical paradigm for collaborative model training in privacy-sensitive domains such as finance and healthcare. However, most exi…
Ali-AUG: Innovative Approaches to Labeled Data Augmentation using One-Step Diffusion Model
Ali Hamza, Aizea Lojo, Adrian Núñez-Marcos +1
This paper introduces Ali-AUG, a novel single-step diffusion model for efficient labeled data augmentation in industrial applications. Our method addresses the challenge of limited…
Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Need
Jon Irureta, Jon Imaz, Aizea Lojo +3
Existing Vertical FL (VFL) methods often struggle with realistic and unaligned data partitions, and incur into high communication costs and significant operational complexity. This…
Quantum artificial vision for defect detection in manufacturing
Daniel Guijo, Victor Onofre, Gianni Del Bimbo +7
In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their…