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

The Position Embedding (PE) is critical for Vision Transformers (VTs) due tothe permutation-invariance of self-attention operation. By analyzing the inputand output of each encoder layer in VTs using reparameterization andvisualization, we find that the default PE joining method (simply adding the PEand patch embedding together) operates the same affine transformation to tokenembedding and PE, which limits the expressiveness of PE and hence constrainsthe performance of VTs. To overcome this limitation, we propose a simple,effective, and robust method. Specifically, we provide two independent layernormalizations for token embeddings and PE for each layer, and add themtogether as the input of each layer's Muti-Head Self-Attention module. Sincethe method allows the model to adaptively adjust the information of PE fordifferent layers, we name it as Layer-adaptive Position Embedding, abbreviatedas LaPE. Extensive experiments demonstrate that LaPE can improve various VTswith different types of PE and make VTs robust to PE types. For example, LaPEimproves 0.94% accuracy for ViT-Lite on Cifar10, 0.98% for CCT on Cifar100, and1.72% for DeiT on ImageNet-1K, which is remarkable considering the negligibleextra parameters, memory and computational cost brought by LaPE. The code ispublicly available at https://github.com/Ingrid725/LaPE.

Position Embedding Needs an Independent Layer Normalization