TL;DR
A continual learning model that effectively leverages the extraction of GCF-based information, enabling it to alleviate catastrophic forgetting through cognitive condensation of GCF, and efficiently manages the rapid growth of stored data points in the storage space.
Experiments show that FiUni can effectively infer latent batch-level task affiliations and achieve competitive performance against advanced task-aware CL methods with fewer trainable parameters.
We explore catastrophic forgetting in the context of large pre-trained models. By considering forgetting as a geometric problem in the input space of each weight matrix, we uncover a natural retention objective under which updates produced by gradient-based optimizers are suboptimal. Following this observation, we prop...
Class Incremental Learning (Class-IL) requires models to learn new classes over time while preserving previously acquired knowledge without access to past data or task identity. This setting intensifies the stability-plasticity dilemma and makes catastrophic forgetting a central challenge. Existing approaches include r...
Parameter-efficient adaptation enables continual learners to acquire task-specific knowledge through compact model updates while maintaining strong within-task performance. However, class-incremental inference requires each input to be classified among all classes seen so far without access to its task identity. For le...
Continual learning requires a model to retain knowledge of old tasks while sequentially learning new tasks, but standard neural networks typically suffer from catastrophic forgetting in this setting. To address this challenge, a new method is proposed based on a learnable knowledge transfer network. Specifically, a tra...
Together, these results show that composing complementary mechanisms substantially improves long-horizon memorization beyond what any individual mechanism achieves.
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