12 citations · 33 across the 11 of their papers we have counts for
11 papers
Continual Learning in the Presence of Repetition
Hamed Hemati, Lorenzo Pellegrini, Xiaotian Duan +11
Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world p…
Sample Weight Estimation Using Meta-Updates for Online Continual Learning
Hamed Hemati, Damian Borth
The loss function plays an important role in optimizing the performance of a learning system. A crucial aspect of the loss function is the assignment of sample weights within a min…
A Comprehensive Empirical Evaluation on Online Continual Learning
Albin Soutif--Cormerais, Antonio Carta, Andrea Cossu +4
Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum a…
Partial Hypernetworks for Continual Learning
Hamed Hemati, Vincenzo Lomonaco, Davide Bacciu +1
Hypernetworks mitigate forgetting in continual learning (CL) by generating task-dependent weights and penalizing weight changes at a meta-model level. Unfortunately, generating all…
Avalanche: A PyTorch Library for Deep Continual Learning
Antonio Carta, Lorenzo Pellegrini, Andrea Cossu +2
Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time. Unf…
Class-Incremental Learning with Repetition
Hamed Hemati, Andrea Cossu, Antonio Carta +5
Real-world data streams naturally include the repetition of previous concepts. From a Continual Learning (CL) perspective, repetition is a property of the environment and, unlike r…
Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
Marco Schreyer, Hamed Hemati, Damian Borth +1
The International Standards on Auditing require auditors to collect reasonable assurance that financial statements are free of material misstatement. At the same time, a central ob…
Generative Data Augmentation Guided by Triplet Loss for Speech Emotion Recognition
Shijun Wang, Hamed Hemati, Jón Guðnason +1
Speech Emotion Recognition (SER) is crucial for human-computer interaction but still remains a challenging problem because of two major obstacles: data scarcity and imbalance. Many…
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
Hamed Hemati, Marco Schreyer, Damian Borth
International audit standards require the direct assessment of a financial statement's underlying accounting journal entries. Driven by advances in artificial intelligence, deep-le…
Continual Speaker Adaptation for Text-to-Speech Synthesis
Hamed Hemati, Damian Borth
Training a multi-speaker Text-to-Speech (TTS) model from scratch is computationally expensive and adding new speakers to the dataset requires the model to be re-trained. The naive…
Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement
Hamed Hemati, Damian Borth
Recent neural Text-to-Speech (TTS) models have been shown to perform very well when enough data is available. However, fine-tuning them for new speakers or languages is not straigh…