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

Neural Representations have recently been shown to effectively reconstruct awide range of signals from 3D meshes and shapes to images and videos. We showthat, when adapted correctly, neural representations can be used to directlyrepresent the weights of a pre-trained convolutional neural network, resultingin a Neural Representation for Neural Networks (NeRN). Inspired by coordinateinputs of previous neural representation methods, we assign a coordinate toeach convolutional kernel in our network based on its position in thearchitecture, and optimize a predictor network to map coordinates to theircorresponding weights. Similarly to the spatial smoothness of visual scenes, weshow that incorporating a smoothness constraint over the original network'sweights aids NeRN towards a better reconstruction. In addition, since slightperturbations in pre-trained model weights can result in a considerableaccuracy loss, we employ techniques from the field of knowledge distillation tostabilize the learning process. We demonstrate the effectiveness of NeRN inreconstructing widely used architectures on CIFAR-10, CIFAR-100, and ImageNet.Finally, we present two applications using NeRN, demonstrating the capabilitiesof the learned representations.

NeRN -- Learning Neural Representations for Neural Networks