1 citations · 1 across the 6 of their papers we have counts for
6 papers
Prototype-Based Continual Learning with Label-free Replay Buffer and Cluster Preservation Loss
Agil Aghasanli, Yi Li, Plamen Angelov
Continual learning techniques employ simple replay sample selection processes and use them during subsequent tasks. Typically, they rely on labeled data. In this paper, we depart f…
Complex-Cycle-Consistent Diffusion Model for Monaural Speech Enhancement
Yi Li, Yang Sun, Plamen Angelov
In this paper, we present a novel diffusion model-based monaural speech enhancement method. Our approach incorporates the separate estimation of speech spectra's magnitude and phas…
Self-Supervised Representation Learning for Adversarial Attack Detection
Yi Li, Plamen Angelov, Neeraj Suri
Supervised learning-based adversarial attack detection methods rely on a large number of labeled data and suffer significant performance degradation when applying the trained model…
PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection
Alvaro Lopez Pellcier, Yi Li, Plamen Angelov
Deepfake techniques generate highly realistic data, making it challenging for humans to discern between actual and artificially generated images. Recent advancements in deep learni…
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification
Alvaro Lopez Pellicer, Kittipos Giatgong, Yi Li +2
As the use of Deep Neural Networks (DNNs) becomes pervasive, their vulnerability to adversarial attacks and limitations in handling unseen classes poses significant challenges. The…
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection
Yi Li, Plamen Angelov, Zhengxin Yu +2
As the development of deep learning techniques in autonomous landing systems continues to grow, one of the major challenges is trust and security in the face of possible adversaria…