TL;DR
RSIAT significantly outperforms state-of-the-art methods in both performance and parameter efficiency, achieving superior stability–plasticity trade-offs with minimal trainable parameters.
Miles decouples the learnable modules with the pre-trained model, exploiting prior information from intermediate features of the backbone network to enable more flexible parameter expansion, and orchestrating an efficient expansion of the parameter space through guided optimization.
Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts. Domain incremental learning (DIL) addresses this challenge by enabling models to continuously adapt while retaining prior knowledge. Among existi...
The proposed CARL framework employs two parallel ARL branches and aggregates their Mahalanobis distances during inference to improve prediction performance and is compared with recent methods using three widely recognized datasets.
Experimental findings show that pretrained models outperform those trained from scratch in terms of accuracy, convergence speed, and robustness, and a unified framework is proposed to integrate both processes in a deep learning pipeline.
This work observes that pooled token embeddings from a frozen LLM embedding layer already separate task distributions throughout the learning sequence, and concludes that a Gaussian mixture model fitted on these embeddings, without any gradient-based training, is sufficient for task-agnostic adapter selection at test t...
TAILS resolves cross-task ambiguity at the representation level, while leaving the original PTM, method-specific modules, and classifier unchanged, and can improve classification and task-inference performance with modest parameter overhead and negligible inference cost.
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