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

Anomaly inspection plays an important role in industrial manufacture.Existing anomaly inspection methods are limited in their performance due toinsufficient anomaly data. Although anomaly generation methods have beenproposed to augment the anomaly data, they either suffer from poor generationauthenticity or inaccurate alignment between the generated anomalies and masks.To address the above problems, we propose AnomalyDiffusion, a noveldiffusion-based few-shot anomaly generation model, which utilizes the strongprior information of latent diffusion model learned from large-scale dataset toenhance the generation authenticity under few-shot training data. Firstly, wepropose Spatial Anomaly Embedding, which consists of a learnable anomalyembedding and a spatial embedding encoded from an anomaly mask, disentanglingthe anomaly information into anomaly appearance and location information.Moreover, to improve the alignment between the generated anomalies and theanomaly masks, we introduce a novel Adaptive Attention Re-weighting Mechanism.Based on the disparities between the generated anomaly image and normal sample,it dynamically guides the model to focus more on the areas with less noticeablegenerated anomalies, enabling generation of accurately-matched anomalousimage-mask pairs. Extensive experiments demonstrate that our modelsignificantly outperforms the state-of-the-art methods in generationauthenticity and diversity, and effectively improves the performance ofdownstream anomaly inspection tasks. The code and data are available inhttps://github.com/sjtuplayer/anomalydiffusion.

AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model