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

Test-Time-Training (TTT) is an approach to cope with out-of-distribution(OOD) data by adapting a trained model to distribution shifts occurring attest-time. We propose to perform this adaptation via Activation Matching(ActMAD): We analyze activations of the model and align activation statisticsof the OOD test data to those of the training data. In contrast to existingmethods, which model the distribution of entire channels in the ultimate layerof the feature extractor, we model the distribution of each feature in multiplelayers across the network. This results in a more fine-grained supervision andmakes ActMAD attain state of the art performance on CIFAR-100C and Imagenet-C.ActMAD is also architecture- and task-agnostic, which lets us go beyond imageclassification, and score 15.4% improvement over previous approaches whenevaluating a KITTI-trained object detector on KITTI-Fog. Our experimentshighlight that ActMAD can be applied to online adaptation in realisticscenarios, requiring little data to attain its full performance.

ActMAD: Activation Matching to Align Distributions for Test-Time-Training