With significant advances in deep learning, many computer vision applications have reached the inflection point. However, these deep learning models need large amount of labeled data for model training and optimum parameter estimation. Limited labeled data for model training results in over-fitting and impacts their generalization performance. However, the collection and annotation of large amount of data is a very time consuming and expensive operation. Further, due to privacy and security concerns, the large amount of labeled data could not be collected for certain applications such as those involving medical field. Self-training, Co-training, and Self-ensemble methods are three types of semi-supervised learning methods that can be used to exploit unlabeled data. In this paper, we propose self-ensemble based deep learning model that along with limited labeled data, harness unlabeled data for improving the generalization performance. We evaluated the proposed self-ensemble based deep-learning model for soft-biometric gender and age classification. Experimental evaluation on CelebA and VISOB datasets suggest gender classification accuracy of 94.46% and 81.00%, respectively, using only 1000 labeled samples and remaining 199k samples as unlabeled samples for CelebA dataset and similarly,1000 labeled samples with remaining 107k samples as unlabeled samples for VISOB dataset. Comparative evaluation suggest that there is $5.74\%$ and $8.47\%$ improvement in the accuracy of the self-ensemble model when compared with supervised model trained on the entire CelebA and VISOB dataset, respectively. We also evaluated the proposed learning method for age-group prediction on Adience dataset and it outperformed the baseline supervised deep-learning learning model with a better exact accuracy of 55.55 $\pm$ 4.28 which is 3.92% more than the baseline.
翻译:随着深层学习的显著进步,许多计算机视觉应用已经达到渗透点。然而,这些深层学习模型需要大量标签值数据,用于模型培训和最佳参数估计。模型培训的标签数据有限,导致模型培训结果过于完善,影响其总体性性能。然而,大量数据的收集和批注是非常耗时和昂贵的操作。此外,由于隐私和安全方面的考虑,无法为某些应用,如医疗领域的应用收集大量标签值数据。自我培训、共同培训和自学模型是三种半监督值的学习方法,可用于模型培训和最佳参数估计。在本文中,我们提出基于自我强化的深层学习模型,连同有限的标签数据一起,利用无标签值数据来改进总体性工作。我们评价了基于深层学习模型的自我强化模型,例如医疗领域。 自我培训、共同培训和自学方法是三种半监督性学习方法,可分别用于开发非标签的精准性精确度数据。