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Found 5 papers, 5 papers with code
Recall-Oriented Continual Learning with Generative Adversarial Meta-Model
The stability-plasticity dilemma is a major challenge in continual learning, as it involves balancing the conflicting objectives of maintaining performance on previous tasks while learning new tasks.
Teacher as a Lenient Expert: Teacher-Agnostic Data-Free Knowledge Distillation
In this paper, we propose the teacher-agnostic data-free knowledge distillation (TA-DFKD) method, with the goal of more robust and stable performance regardless of teacher models.
Better Generalized Few-Shot Learning Even Without Base Data
In this paper, we overcome this limitation by proposing a simple yet effective normalization method that can effectively control both mean and variance of the weight distribution of novel classes without using any base samples and thereby achieve a satisfactory performance on both novel and base classes.
Ranked #1 on
Generalized Few-Shot Learning
on CUB
Pool of Experts: Realtime Querying Specialized Knowledge in Massive Neural Networks
In spite of the great success of deep learning technologies, training and delivery of a practically serviceable model is still a highly time-consuming process.
Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network
Continual learning has been a major problem in the deep learning community, where the main challenge is how to effectively learn a series of newly arriving tasks without forgetting the knowledge of previous tasks.
Ranked #1 on
Class Incremental Learning
on cifar100
