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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.

 Ranked #1 on Object Detection on CrowdHuman (full body) (AP 0.5 metric)

DATA AUGMENTATION REAL-TIME OBJECT DETECTION

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.

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

Supervised Contrastive Learning

NeurIPS 2020 google-research/google-research

Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models.

DATA AUGMENTATION IMAGE CLASSIFICATION REPRESENTATION LEARNING SELF-SUPERVISED LEARNING

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

31 Aug 2020PyTorchLightning/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.

4 DATA AUGMENTATION IMAGE CLASSIFICATION SELF-SUPERVISED LEARNING

EfficientNetV2: Smaller Models and Faster Training

1 Apr 2021rwightman/pytorch-image-models

By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87. 3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2. 0% accuracy while training 5x-11x faster using the same computing resources.

DATA AUGMENTATION IMAGE CLASSIFICATION NEURAL ARCHITECTURE SEARCH

RandAugment: Practical automated data augmentation with a reduced search space

NeurIPS 2020 rwightman/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

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