English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent findings have shown that highly over-parameterized Neural Networksgeneralize without pretraining or explicit regularization. It is achieved withzero training error, i.e., complete over-fitting by memorizing the trainingdata. This is surprising, since it is completely against traditional machinelearning wisdom. In our empirical study we fortify these findings in the domainof fine-grained image classification. We show that very large ConvolutionalNeural Networks with millions of weights do learn with only a handful oftraining samples and without image augmentation, explicit regularization orpretraining. We train the architectures ResNet018, ResNet101 and VGG19 onsubsets of the difficult benchmark datasets Caltech101, CUB_200_2011,FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform acomprehensive comparative study and draw implications for the practicalapplication of CNNs. Finally, we show that a randomly initialized VGG19 with140 million weights learns to distinguish airplanes and motorbikes with up to95% accuracy using only 20 training samples per class.

Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization