针对中文文本情感分类任务的优化需求,研究Transformer模型基本结构及其在情感分类任务中的应用,提出基于梯度范数感知的分层自适应余弦退火方法来优化传统余弦退火学习率调度策略。文章基于ChnSentiCorp数据集在Hugging Face Transform...针对中文文本情感分类任务的优化需求,研究Transformer模型基本结构及其在情感分类任务中的应用,提出基于梯度范数感知的分层自适应余弦退火方法来优化传统余弦退火学习率调度策略。文章基于ChnSentiCorp数据集在Hugging Face Transformers框架下进行实验。实验结果表明,文章方法在准确率、精确率、召回率方面均优于传统方法。展开更多
Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this wo...Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this work proposes a new hybrid λ2-λ0 penalty model for image dehazing. This model performs a weighted fusion of two distinct transmission maps, generated by imposing λ2 and λ0 norm penalties on the approximate regression coefficients of the transmission map. This approach effectively balances the sparsity and smoothness associated with the λ0 and λ2 norms, thereby optimizing the transmittance map. Specifically, when the λ2 norm is penalized in the model, an updated guided image is obtained after implementing λ0 penalty. The resulting optimization problem is effectively solved using the least square method and the alternating direction algorithm. The dehazing framework combines the advantages of λ2 and λ0 norms, enhancing sparse and smoothness, resulting in higher quality images with clearer details and preserved edges.展开更多
文摘针对中文文本情感分类任务的优化需求,研究Transformer模型基本结构及其在情感分类任务中的应用,提出基于梯度范数感知的分层自适应余弦退火方法来优化传统余弦退火学习率调度策略。文章基于ChnSentiCorp数据集在Hugging Face Transformers框架下进行实验。实验结果表明,文章方法在准确率、精确率、召回率方面均优于传统方法。
文摘Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this work proposes a new hybrid λ2-λ0 penalty model for image dehazing. This model performs a weighted fusion of two distinct transmission maps, generated by imposing λ2 and λ0 norm penalties on the approximate regression coefficients of the transmission map. This approach effectively balances the sparsity and smoothness associated with the λ0 and λ2 norms, thereby optimizing the transmittance map. Specifically, when the λ2 norm is penalized in the model, an updated guided image is obtained after implementing λ0 penalty. The resulting optimization problem is effectively solved using the least square method and the alternating direction algorithm. The dehazing framework combines the advantages of λ2 and λ0 norms, enhancing sparse and smoothness, resulting in higher quality images with clearer details and preserved edges.
