arXiv:2501.00340v1 Announce Type: new Abstract: Significant advancements have been made in single label incremental learning (SLCIL),yet the more practical and challenging multi label class incremental learning (MLCIL) remains understudied. Recently,visual language models such as CLIP have achieved good results in classification tasks. However,directly using CLIP to solve MLCIL issue can lead to catastrophic forgetting. To tackle this issue, we integrate an improved data replay mechanism and prompt loss to curb knowledge forgetting. Specifically,our model enhances the prompt information to better adapt to multi-label classification tasks and employs confidence-based replay strategy to select representative samples. Moreover, the prompt loss significantly reduces the model’s forgetting of previous knowledge. Experimental results demonstrate that our method has substantially improved the performance of MLCIL tasks across multiple benchmark datasets,validating its effectiveness.
This article explores the challenges and advancements in multi-label class incremental learning (MLCIL), which has received less attention compared to single-label incremental learning (SLCIL). While visual language models like CLIP have achieved success in classification tasks, directly applying CLIP to MLCIL can result in catastrophic forgetting. To address this issue, the authors propose an integrated approach that combines an improved data replay mechanism and prompt loss to prevent knowledge forgetting. Their model enhances prompt information for multi-label classification and uses a confidence-based replay strategy to select representative samples. Experimental results demonstrate the effectiveness of their method in improving the performance of MLCIL tasks across various benchmark datasets.
Exploring Multi-Label Class Incremental Learning Using Visual Language Models
Machine learning advancements have paved the way for various classification tasks. One notable development is single label incremental learning (SLCIL), which has seen significant progress in recent times. However, the more complex and practical multi-label class incremental learning (MLCIL) remains relatively understudied. Emerging visual language models like CLIP have showcased promising results in classification tasks, but utilizing CLIP directly for MLCIL can lead to a phenomenon known as catastrophic forgetting.
Catastrophic forgetting occurs when a model becomes incapable of accurately recalling previously learned information after acquiring new knowledge. This poses a challenge in MLCIL, as the model needs to continually adapt to evolving classes while retaining its understanding of previously encountered labels.
To address this issue, we propose a novel approach that integrates an improved data replay mechanism and prompt loss within the framework of CLIP. By enhancing the prompt information, our model better adapts to multi-label classification tasks. Additionally, we employ a confidence-based replay strategy to select representative samples, ensuring that the model retains crucial knowledge while accommodating new information.
The inclusion of prompt loss significantly reduces the model’s tendency to forget previously learned knowledge. By minimizing the loss associated with prompts, the model becomes more reliable in recalling past labels, leading to enhanced performance in MLCIL tasks.
We conducted extensive experiments using multiple benchmark datasets to validate the efficacy of our method. The results demonstrate a substantial improvement in the performance of MLCIL tasks. The integration of improved data replay, prompt loss, and enhanced prompt information effectively mitigates catastrophic forgetting, enabling the model to continually learn new multi-label classifications while retaining valuable knowledge from previous classes.
Our approach opens up new avenues for research in MLCIL tasks and provides valuable insights into the application of visual language models like CLIP in complex classification scenarios. By addressing the challenges of catastrophic forgetting, we lay the foundation for future advancements in multi-label class incremental learning, further enriching the capabilities of machine learning models.
The paper titled “Significant advancements have been made in single label incremental learning (SLCIL), yet the more practical and challenging multi label class incremental learning (MLCIL) remains understudied” highlights the need for research in multi-label class incremental learning, a domain that has received less attention compared to single-label incremental learning.
The authors acknowledge the success of visual language models like CLIP in classification tasks. However, they point out that directly using CLIP for multi-label class incremental learning can result in catastrophic forgetting, where the model forgets previously learned knowledge when new labels are introduced.
To address this issue, the authors propose an approach that combines an improved data replay mechanism and prompt loss. The improved data replay mechanism helps the model retain knowledge by selectively replaying representative samples from previous tasks. This strategy ensures that important information is not forgotten when new labels are introduced.
In addition, the authors introduce a prompt loss that aims to reduce the model’s forgetting of previous knowledge. By enhancing the prompt information to better adapt to multi-label classification tasks, the model can retain knowledge while learning new labels.
The experimental results presented in the paper demonstrate the effectiveness of the proposed method. The approach significantly improves the performance of multi-label class incremental learning tasks across multiple benchmark datasets.
Overall, this research addresses an important gap in the field of incremental learning by focusing on multi-label classification tasks. By integrating an improved data replay mechanism and prompt loss, the authors provide a promising solution to mitigate catastrophic forgetting. This work opens up new possibilities for developing more robust models that can incrementally learn multiple labels without sacrificing the knowledge gained from previous tasks.
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