Face recognitionKnowledge distillationWeight exclusivityFeature consistencyKnowledge distillation is an effective tool to compress large pre-trained Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile and embedded devices. The success of which mainly comes from two aspects...
此外,它对新数据表现出一定的过拟合,导致与域内数据集相比,在域外数据集上的性能较差。 知识蒸馏Knowledge Distillation (KD)旨在将一个大型的预训练模型(教师)的知识转移到一个更简单、更资源高效的模型(学生)中,以便实现高效部署[13]。这使得学生模型可以从教师模型中学习特征表示,并最小化它们之间的差异。KD最...
Face recognition has significantly improved with the development of deep learning technology. However, in the case of a viral epidemic like COVID-19, wearing masks reduces the risk of infection significantly but results in losing crucial face features and increasing intra-class divergence, which decre...
【distill.&transfer】Deep Face Recognition Model Compression via Knowledge Transfer and Distillation,程序员大本营,技术文章内容聚合第一站。
Online knowledge distillationIdentity-irrelevant similarity lossHigh-fidelity lightweight generatorAdvanced face swapping approaches have achieved high-fidelity results. However, the success of most methods hinges on heavy parameters and high-computational costs. With the popularity of real-time face swapping...
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Face recognition systems have been the subject of research for more than 40 years. Recent advances in machine learning have led to a dramatic increase in research and the emergence of many successful approaches. Facial recognition or identification technology is more important today than any time in...
可以看到整个思路下来还是比较简单明了的,但是感觉整个网络非常的庞大,每一个region用一个subnet处理,这样参数量就会成倍的增加。作者也考虑到了这个问题所以用knowledge distillation去压缩ACM中的subnet。 作为本文主打的一个idea - unaligned face attribute classification,主要就是通过localization network(LN)实现的,这个...
2.3. Lightweight Network for Face Recognition 2.3.1 Head setting 人脸识别的主要挑战是测试/训练阶段涉及的大规模身份识别。它需要尽可能多的辨别能力来支持区分数百万种身份。为了在轻量级网络中保留这种能力,我们在网络开始时使用stride=1的3×3 卷积层,然后在VarGNet中使用stride=2的3×3卷积。它类似于[3]的...
LIAAD [14] developed a novel, lightweight attention-based method that, through knowledge distillation, improves accuracy and robustness against age variations in face recognition. Zhang et al. delved into face recognition and its applications, including expression recognition [15], generation [15], ...