Search Results for author:
Found 10 papers, 4 papers with code
Diffexplainer: Towards Cross-modal Global Explanations with Diffusion Models
We present DiffExplainer, a novel framework that, leveraging language-vision models, enables multimodal global explainability.
Selective Attention-based Modulation for Continual Learning
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting.
Wake-Sleep Consolidated Learning
In the REM stage, the model is exposed to previously-unseen realistic visual sensory experience, and the dreaming process is activated, which enables the model to explore the potential feature space, thus preparing synapses to future knowledge.
A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications
Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution.
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning
Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically.
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis
In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data.
Effects of Auxiliary Knowledge on Continual Learning
In this paper we propose a new, simple, CL algorithm that focuses on solving the current task in a way that might facilitate the learning of the next ones.
Transfer without Forgetting
This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL).
Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 AI diagnosis
This is the leitmotif of the convergence between HPC and AI.
An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans
In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans.
