In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.
SOTA for Image Classification on SVHN
We provide examples of image augmentations for different computer vision tasks and show that Albumentations is faster than other commonly used image augmentation tools on the most of commonly used image transformations.
In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).
#2 best model for Image Classification on Fashion-MNIST
Importantly, the best policy found on COCO may be transferred unchanged to other detection datasets and models to improve predictive accuracy.
#9 best model for Object Detection on COCO test-dev
There is large consent that successful training of deep networks requires many thousand annotated training samples.
SOTA for Cell Segmentation on PhC-U373
CELL SEGMENTATION COLORECTAL GLAND SEGMENTATION: ELECTRON MICROSCOPY IMAGE SEGMENTATION IMAGE AUGMENTATION LESION SEGMENTATION LUNG NODULE SEGMENTATION MULTI-TISSUE NUCLEUS SEGMENTATION PANCREAS SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION
In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.
#2 best model for Text Classification on IMDb
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations.
#3 best model for Image Classification on SVHN
Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks.
#3 best model for Semi-Supervised Image Classification on STL-10
An agent network learns from the output of the recognition network and controls the fiducial points to generate more proper training samples for the recognition network.