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

For the past 5 years, the ILSVRC competition and the ImageNet dataset haveattracted a lot of interest from the Computer Vision community, allowing forstate-of-the-art accuracy to grow tremendously. This should be credited to theuse of deep artificial neural network designs. As these became more complex,the storage, bandwidth, and compute requirements increased. This means thatwith a non-distributed approach, even when using the most high-density serveravailable, the training process may take weeks, making it prohibitive.Furthermore, as datasets grow, the representation learning potential of deepnetworks grows as well by using more complex models. This synchronicitytriggers a sharp increase in the computational requirements and motivates us toexplore the scaling behaviour on petaflop scale supercomputers. In this paperwe will describe the challenges and novel solutions needed in order to trainResNet-50 in this large scale environment. We demonstrate above 90\% scalingefficiency and a training time of 28 minutes using up to 104K x86 cores. Thisis supported by software tools from Intel's ecosystem. Moreover, we show thatwith regular 90 - 120 epoch train runs we can achieve a top-1 accuracy as highas 77\% for the unmodified ResNet-50 topology. We also introduce the novelCollapsed Ensemble (CE) technique that allows us to obtain a 77.5\% top-1accuracy, similar to that of a ResNet-152, while training a unmodifiedResNet-50 topology for the same fixed training budget. All ResNet-50 models aswell as the scripts needed to replicate them will be posted shortly.

Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train