Papers Continual Self-Supervised Learning
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Continual Self-Supervised Learning with Masked Autoencoders in Remote Sensing
The development of continual learning (CL) methods, which aim to learn new tasks in a sequential manner from the training data acquired continuously, has gained great attention in remote sensing (RS). The existing CL met…
Continual LearningContinual Self-Supervised LearningKnowledge DistillationSelf-Supervised LearningA Plasticity-Aware Method for Continual Self-Supervised Learning in Remote Sensing
Continual self-supervised learning (CSSL) methods have gained increasing attention in remote sensing (RS) due to their capability to learn new tasks sequentially from continuous streams of unlabeled data. Existing CSSL m…
Continual Self-Supervised LearningKnowledge DistillationSelf-Supervised LearningContinual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images
We propose a novel continual self-supervised learning method (CSSL) considering medical domain knowledge in chest CT images. Our approach addresses the challenge of sequential learning by effectively capturing the relati…
Continual Self-Supervised LearningDiversitySelf-Supervised LearningCroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning
Continual self-supervised learning (CSSL) learns a series of tasks sequentially on the unlabeled data. Two main challenges of continual learning are catastrophic forgetting and task confusion. While CSSL problem has been…
class-incremental learningClass Incremental LearningContinual LearningContinual Self-Supervised Learning+2LGSDF: Continual Global Learning of Signed Distance Fields Aided by Local Updating
Implicit reconstruction of ESDF (Euclidean Signed Distance Field) involves training a neural network to regress the signed distance from any point to the nearest obstacle, which has the advantages of lightweight storage …
Continual Self-Supervised LearningSelf-Supervised LearningBranch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning
Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. However, as real-world applications continually integrate new content, the hig…
Continual LearningContinual Self-Supervised LearningSelf-Supervised LearningBalancing Continual Learning and Fine-tuning for Human Activity Recognition
Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature of human behaviours, continual learning …
Activity RecognitionContinual LearningContinual Self-Supervised LearningContrastive Learning+3Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning
Self-supervised learning is an efficient pre-training method for medical image analysis. However, current research is mostly confined to specific-modality data pre-training, consuming considerable time and resources with…
Continual LearningContinual Self-Supervised LearningMedical Image AnalysisRepresentation Learning+1Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning
We introduce a novel Pseudo-Negative Regularization (PNR) framework for effective continual self-supervised learning (CSSL). Our PNR leverages pseudo-negatives obtained through model-based augmentation in a way that newl…
Continual LearningContinual Self-Supervised LearningContrastive LearningImage Classification+2CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition
Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised met…
Continual Self-Supervised LearningHandwritten Text RecognitionSelf-Supervised LearningContrastive Continuity on Augmentation Stability Rehearsal for Continual Self-Supervised Learning
Self-supervised learning has attracted a lot of attention recently, which is able to learn powerful representations without any manual annotations. However, self-supervised learning needs to develop the ability to co…
Continual Self-Supervised LearningSelf-Supervised LearningIs Multi-Task Learning an Upper Bound for Continual Learning?
Continual and multi-task learning are common machine learning approaches to learning from multiple tasks. The existing works in the literature often assume multi-task learning as a sensible performance upper bound for va…
Continual LearningContinual Self-Supervised LearningMulti-Task LearningSelf-Supervised LearningContinual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation
In the field of Earth Observation (EO), Continual Learning (CL) algorithms have been proposed to deal with large datasets by decomposing them into several subsets and processing them incrementally. The majority of these …
Continual LearningContinual Self-Supervised LearningEarth ObservationSelf-Supervised Learning+1Learning Representations for New Sound Classes With Continual Self-Supervised Learning
In this paper, we work on a sound recognition system that continually incorporates new sound classes. Our main goal is to develop a framework where the model can be updated without relying on labeled data. For this purpo…
Continual LearningContinual Self-Supervised LearningRepresentation LearningSelf-Supervised Learning