Papers Split-CIFAR-10
“Split-CIFAR-10” 태그가 달린 논문 10편 · 필터 해제
Enhancing Robustness in Incremental Learning with Adversarial Training
Adversarial training is one of the most effective approaches against adversarial attacks. However, adversarial training has primarily been studied in scenarios where data for all classes is provided, with limited researc…
Adversarial Robustnessclass-incremental learningClass Incremental LearningIncremental Learning+1Negotiated Representations to Prevent Forgetting in Machine Learning Applications
Catastrophic forgetting is a significant challenge in the field of machine learning, particularly in neural networks. When a neural network learns to perform well on a new task, it often forgets its previously acquired k…
Continual LearningImage ClassificationSplit-CIFAR-10Split-MNISTOvercoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
Continual learning entails learning a sequence of tasks and balancing their knowledge appropriately. With limited access to old training samples, much of the current work in deep neural networks has focused on overcoming…
Continual LearningSplit-CIFAR-10Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical to designing intelligent systems. Many approaches to continual learning rely on stochastic gradient descent and i…
Continual LearningSplit-CIFAR-10Split-MNISTMixture-of-Variational-Experts for Continual Learning
One weakness of machine learning algorithms is the poor ability of models to solve new problems without forgetting previously acquired knowledge. The Continual Learning (CL) paradigm has emerged as a protocol to systemat…
Continual LearningDomain-IL Continual LearningPermuted-MNISTreinforcement-learning+4Dendritic Self-Organizing Maps for Continual Learning
Current deep learning architectures show remarkable performance when trained in large-scale, controlled datasets. However, the predictive ability of these architectures significantly decreases when learning new classes i…
Continual LearningSplit-CIFAR-10Split-MNISTHypernetworks for Continual Semi-Supervised Learning
Learning from data sequentially arriving, possibly in a non i.i.d. way, with changing task distribution over time is called continual learning. Much of the work thus far in continual learning focuses on supervised learni…
Continual LearningGenerative Adversarial NetworkSplit-CIFAR-10Self-Attention Meta-Learner for Continual Learning
Continual learning aims to provide intelligent agents capable of learning multiple tasks sequentially with neural networks. One of its main challenging, catastrophic forgetting, is caused by the neural networks non-optim…
Continual LearningSplit-CIFAR-10Split-MNISTNeuromodulated Neural Architectures with Local Error Signals for Memory-Constrained Online Continual Learning
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical for designing intelligent systems. Many existing approaches to continual learning rely on stochastic gradient des…
Bayesian OptimizationClass Incremental LearningContinual LearningEdge-computing+3Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
A central challenge in developing versatile machine learning systems is catastrophic forgetting: a model trained on tasks in sequence will suffer significant performance drops on earlier tasks. Despite the ubiquity of ca…
AnatomySplit-CIFAR-10