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Efficient Prompt Learning Method in Blurry Class Incremental Learning Environment
http://doi.org/10.5626/JOK.2024.51.7.655
Continual learning is the process of continuously integrating new knowledge to maintain performance across a sequence of tasks. While disjoint continual learning, which assumes no overlap between classes across tasks, blurry continual learning addresses more realistic scenarios where overlaps do exist. Traditionally, most related works have predominantly focused on disjoint scenarios and recent attention has shifted towards prompt-based continual learning. This approach uses prompt mechanism within a Vision Transformer (ViT) model to improve adaptability. In this study, we analyze the effectiveness of a similarity function designed for blurry class incremental learning, applied within a prompt-based continual learning framework. Our experiments demonstrate the success of this method, particularly in its superior ability to learn from and interpret blurry data.
A Token Selection Method for Effective Token Pruning in Vision Transformers
http://doi.org/10.5626/JOK.2024.51.6.567
The self-attention-based models, vision transformers, have recently been employed in the field of computer vision. While achieving excellent performance in a variety of tasks, the computation costs increase in proportion to the number of tokens during inference, which causes a degradation in inference speed. Especially when deploying the model in real-world scenarios, many limitations could be encountered. To address this issue, we propose a new token importance measurement, which can be obtained by modifying the structure of multi-head self-attention in vision transformers. By pruning less important tokens through our method during inference, we can improve inference speed while preserving performance. Furthermore, our proposed method, which requires no additional parameters, exhibits better robustness without fine-tuning and demonstrates that it can maximize performance when integrated with existing token pruning methods.
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