2024/01/11 by Weijieying Ren, Vasant Honavar, Ren, Weijieying +1
Computer Science · #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Forgetting #Human Pose and Action Recognition #Key (lock) #Machine Learning (cs.LG) #Machine learning #Multimodal Machine Learning Applications #Pruning #Retraining
paper · pdf · doi:10.48550/arxiv.2401.05667
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/01/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
A key challenge in the continual learning setting is to efficiently learn a sequence of tasks without forgetting how to perform previously learned tasks. Many existing approaches to this problem work by either retraining the model on previous tasks or by expanding the model to accommodate new tasks. However, these approaches typically suffer from increased storage and computational requirements, a problem that is worsened in the case of sparse models due to need for expensive re-training after sparsification. To address this challenge, we propose a new method for efficient continual learning of sparse models (EsaCL) that can automatically prune redundant parameters without adversely impacting the model's predictive power, and circumvent the need of retraining. We conduct a theoretical analysis of loss landscapes with parameter pruning, and design a directional pruning (SDP) strategy that is informed by the sharpness of the loss function with respect to the model parameters. SDP ensures model with minimal loss of predictive accuracy, accelerating the learning of sparse models at each stage. To accelerate model update, we introduce an intelligent data selection (IDS) strategy that can identify critical instances for estimating loss landscape, yielding substantially improved data efficiency. The results of our experiments show that EsaCL achieves performance that is competitive with the state-of-the-art methods on three continual learning benchmarks, while using substantially reduced memory and computational resources.