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Data Augmentation

525 papers with code · Methodology

( Image credit: Albumentations )

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Greatest papers with code

AutoAugment: Learning Augmentation Policies from Data

24 May 2018tensorflow/models

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.

FINE-GRAINED IMAGE CLASSIFICATION IMAGE AUGMENTATION

YOLOv4: Optimal Speed and Accuracy of Object Detection

23 Apr 2020pjreddie/darknet

There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy.

DATA AUGMENTATION REAL-TIME OBJECT DETECTION ADVERSARIAL TRAINING

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

18 Apr 2019mozilla/DeepSpeech

On LibriSpeech, we achieve 6. 8% WER on test-other without the use of a language model, and 5. 8% WER with shallow fusion with a language model.

Ranked #2 on Speech Recognition on Hub5'00 SwitchBoard (SwitchBoard metric)

DATA AUGMENTATION END-TO-END SPEECH RECOGNITION LANGUAGE MODELLING SPEECH RECOGNITION

Large Margin Deep Networks for Classification

NeurIPS 2018 google-research/google-research

We present a formulation of deep learning that aims at producing a large margin classifier.

DATA AUGMENTATION

A Framework For Contrastive Self-Supervised Learning And Designing A New Approach

ICLR 2021 PyTorchLightning/pytorch-lightning

Contrastive self-supervised learning (CSL) is an approach to learn useful representations by solving a pretext task that selects and compares anchor, negative and positive (APN) features from an unlabeled dataset.

DATA AUGMENTATION IMAGE CLASSIFICATION SELF-SUPERVISED LEARNING

Albumentations: fast and flexible image augmentations

18 Sep 2018albu/albumentations

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.

IMAGE AUGMENTATION

RandAugment: Practical automated data augmentation with a reduced search space

30 Sep 2019rwightman/pytorch-image-models

Additionally, due to the separate search phase, these approaches are unable to adjust the regularization strength based on model or dataset size.

DATA AUGMENTATION IMAGE CLASSIFICATION OBJECT DETECTION

Random Erasing Data Augmentation

16 Aug 2017rwightman/pytorch-image-models

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).

IMAGE AUGMENTATION IMAGE CLASSIFICATION OBJECT DETECTION PERSON RE-IDENTIFICATION

Self-training with Noisy Student improves ImageNet classification

CVPR 2020 tensorflow/tpu

During the learning of the student, we inject noise such as dropout, stochastic depth, and data augmentation via RandAugment to the student so that the student generalizes better than the teacher.

Ranked #2 on Image Classification on ImageNet (using extra training data)

DATA AUGMENTATION IMAGE CLASSIFICATION

Learning Data Augmentation Strategies for Object Detection

ECCV 2020 tensorflow/tpu

Importantly, the best policy found on COCO may be transferred unchanged to other detection datasets and models to improve predictive accuracy.

IMAGE AUGMENTATION IMAGE CLASSIFICATION OBJECT DETECTION