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Catastrophic Forgetting in Continual Concept Bottleneck Models
Almost all Deep Learning models are dramatically affected by Catastrophic Forgetting when learning over continual streams of data. To mitigate this...
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GRU-Attention Interpretable Knowledge Tracking Model with Forgetting Law for Intelligent Education System
The advent of intelligent education systems and widespread distance learning have revolutionized the educational landscape. Extracting meaningful...
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Syntactic ASP Forgetting with Forks
In this paper, we present a syntactic transformation, called the unfolding operator, that allows forgetting an atom in a logic program (under ASP...
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Distilled Replay: Overcoming Forgetting Through Synthetic Samples
Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by kee** a buffer of patterns from previous experiences,...
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Balancing Between Forgetting and Acquisition in Incremental Subpopulation Learning
The subpopulation shifting challenge, known as some subpopulations of a category that are not seen during training, severely limits the...
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Overcoming Catastrophic Forgetting for Fine-Tuning Pre-trained GANs
The great transferability of DNNs has induced a popular paradigm of “pre-training & fine-tuning”, by which a data-scarce task can be performed much...
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Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks
Using task-specific components within a neural network in continual learning (CL) is a compelling strategy to address the stability-plasticity...
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Metric Learning with Distillation for Overcoming Catastrophic Forgetting
In incremental learning, reducing catastrophic forgetting always adds additional weights to the classification layer when a new task comes. Moreover,...
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Knowledge Lock: Overcoming Catastrophic Forgetting in Federated Learning
Federated Learning (FL) aims to train machine learning models by decentralized data without direct data sharing. Nevertheless, the heterogeneity of...
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Mitigating Catastrophic Forgetting in Neural Machine Translation Through Teacher-Student Distillation with Attention Mechanism
The catastrophic forgetting is a critical problem for deep learning models, where the models learning a sequence of tasks forgets the previously...
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Cloud Manufacturing Workflow Scheduling with Learning and Forgetting Effects
How to schedule workflow tasks which are constrained by the workflow deadline to minimize the total cost is of great challenge in cloud...
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Partially Relaxed Masks for Knowledge Transfer Without Forgetting in Continual Learning
The existing research on continual learning (CL) has focused mainly on preventing catastrophic forgetting. In the task-incremental learning setting...
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Overcoming Forgetting in Local Adaptation of Federated Learning Model
Federated learning allows multiple clients to train a global model without data exchanging. But in real world, the global model is not suitable for...
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Understanding the Benefits of Forgetting When Learning on Dynamic Graphs
In order to solve graph-related tasks such as node classification, recommendation or community detection, most machine learning algorithms are based...
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Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers
Pretraining language models on large text corpora is a common practice in natural language processing. Fine-tuning of these models is then performed...
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Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training...
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FES-Based Hand Movement Control via Iterative Learning Control with Forgetting Factor
Functional electrical stimulation (FES) is an effective approach to restore hand movement function for patients with stroke. In this paper, a...
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Style Expansion Without Forgetting for Handwritten Character Recognition
Handwritten character recognition (HCR) is still a challenging task due to diverse writing styles. In existing works, the recognition models for...
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Semantic Forgetting in Expressive Description Logics
Forgetting is an important ontology extraction technology. We present a semantic forgetting method for...
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Enhancing network modularity to mitigate catastrophic forgetting
Catastrophic forgetting occurs when learning algorithms change connections used to encode previously acquired skills to learn a new skill. Recently,...
