Search Results for author:
Found 12 papers, 8 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.
TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation
Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications.
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
Early detection of precancerous cysts or neoplasms, i. e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome.
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).
Hierarchical 3D Feature Learning for Pancreas Segmentation
We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans.
Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction
When the base hierarchical model is empowered with domain-specific modules, performance improves, outperforming state-of-the-art models on three out of five metrics on the DHF1K benchmark and reaching the second-best results on the other two.
