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hi everyone today I will talk about our recent work Sita single image texture translation for data augmentation a general solution to examine how SATA and related image translation methods can provide a basis for data efficient augmentation engineering approach to model training in the wild its very hard and impractical to connect a balance the data sets for training recognition models for example connecting healthy leaves is easy while collecting sickness could be comparatively very difficult and expensive our goal is to study the potential use of image synthesis methods for recognition tasks here are two key limitations first the validity of synthetic data for Target labels second the running time to solve this problem we select data augmentation strategy as our primary Direction we provide a simplified illustration of the data augmentation landscape in the figure data augmentation plays a critical role in various image recognition tasks on the one hand basic image manipulation met
