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Found 9 papers, 6 papers with code
Sample Weight Estimation Using Meta-Updates for Online Continual Learning
This is done by first estimating sample weight parameters for each sample in the mini-batch, then, updating the model with the adapted sample weights.
A Comprehensive Empirical Evaluation on Online Continual Learning
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 amount of data from that stream.
Partial Hypernetworks for Continual Learning
Inspired by latent replay methods in CL, we propose partial weight generation for the final layers of a model using hypernetworks while freezing the initial layers.
Avalanche: A PyTorch Library for Deep Continual Learning
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.
Class-Incremental Learning with Repetition
We propose two stochastic stream generators that produce a wide range of CIR streams starting from a single dataset and a few interpretable control parameters.
Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
Our empirical results, using real-world datasets and combined federated continual learning strategies, demonstrate the learned model's ability to detect anomalies in audit settings of data distribution shifts.
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
This work proposes a continual anomaly detection framework to overcome both challenges and designed to learn from a stream of journal entry data experiences.
Continual Speaker Adaptation for Text-to-Speech Synthesis
The naive solution of sequential fine-tuning of a model for new speakers can lead to poor performance of older speakers.
Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement
Recent neural Text-to-Speech (TTS) models have been shown to perform very well when enough data is available.
