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Found 16 papers, 5 papers with code
Few-Shot Continual Learning via Flat-to-Wide Approaches
This paper proposes a few-shot continual learning approach, termed FLat-tO-WidE AppRoach (FLOWER), where a flat-to-wide learning process finding the flat-wide minima is proposed to address the catastrophic forgetting problem.
Assessor-Guided Learning for Continual Environments
This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pace of the learning process thus allowing an efficient learning of new environments while protecting against the catastrophic interference problem.
Evolving Multi-Label Fuzzy Classifier
Furthermore, our approach comes with an online active learning (AL) strategy for updating the classifier on just a number of selected samples, which in turn makes the approach applicable for scarcely labelled streams in applications, where the annotation effort is typically expensive.
Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach
Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground truth incurring expensive labelling cost.
Autonomous Deep Quality Monitoring in Streaming Environments
The common practice of quality monitoring in industry relies on manual inspection well-known to be slow, error-prone and operator-dependent.
Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams
The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform.
DEVDAN: Deep Evolving Denoising Autoencoder
The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples.
ATL: Autonomous Knowledge Transfer from Many Streaming Processes
It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain.
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams
The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples.
An Online RFID Localization in the Manufacturing Shopfloor
{Radio Frequency Identification technology has gained popularity for cheap and easy deployment.
Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment
A novel approach for unsupervised domain adaptation for neural networks is proposed.
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort.
Evolving Ensemble Fuzzy Classifier
pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure.
Metacognitive Learning Approach for Online Tool Condition Monitoring
The learning process consists of three phases: what to learn, how to learn, when to learn and makes use of a generalized recurrent network structure as a cognitive component.
Parsimonious Random Vector Functional Link Network for Data Streams
The theory of random vector functional link network (RVFLN) has provided a breakthrough in the design of neural networks (NNs) since it conveys solid theoretical justification of randomized learning.
Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
We prove that CMD is a metric on the set of probability distributions on a compact interval.
