Source-linked AI summary
Data Augmentation by Pairing Samples for Images Classification
Hiroshi Inoue
TL;DR
Limited training data makes generalization difficult, motivating augmentation beyond conventional image transformations. The paper introduces SamplePairing, which averages randomly selected training-image pairs, and reports improved classification accuracy, particularly for small datasets, while noting that its explanation remains empirical.
Problem
Limited training samples make high-performing, well-generalized image classifiers difficult to train, motivating methods that virtually enlarge the training set.
Method
SamplePairing averages two randomly selected training images pixelwise before classification, using the first image's label for the mixed sample.
Results
SamplePairing reduced validation classification error across all evaluated datasets, including 43.1% to 31.0% with 100 samples per CIFAR-10 label.
Takeaways & Limitations
The technique is especially valuable when training data are limited, including potential applications such as medical image classification.
Takeaways & Limitations
The paper provides empirical evidence without strong theoretical explanations or proof of why SamplePairing improves generalization.
Abstract
from arXiv · showhide
Data augmentation is a widely used technique in many machine learning tasks, such as image classification, to virtually enlarge the training dataset size and avoid overfitting. Traditional data augmentation techniques for image classification tasks create new samples from the original training data by, for example, flipping, distorting, adding a small amount of noise to, or cropping a patch from an original image. In this paper, we introduce a simple but surprisingly effective data augmentation technique for image classification tasks. With our technique, named SamplePairing, we synthesize a new sample from one image by overlaying another image randomly chosen from the training data (i.e., taking an average of two images for each pixel). By using two images randomly selected from the training set, we can generate $N^2$ new samples from $N$ training samples. This simple data augmentation technique significantly improved classification accuracy for all the tested datasets; for example, the top-1 error rate was reduced from 33.5% to 29.0% for the ILSVRC 2012 dataset with GoogLeNet and from 8.22% to 6.93% in the CIFAR-10 dataset. We also show that our SamplePairing technique largely improved accuracy when the number of samples in the training set was very small. Therefore, our technique is more valuable for tasks with a limited amount of training data, such as medical imaging tasks.
1. Introduction
SamplePairing augments image-classification data by averaging each image with another randomly selected training image, yielding significant accuracy gains across tested datasets, especially with limited training data.
- SamplePairing overlays a randomly selected training image onto a base image by averaging corresponding pixel intensities.
- N^2 paired samples can be generated from N training samples, while retaining the base image's label for each mixed sample.
- SamplePairing improved classification accuracy across ILSVRC 2012, CIFAR-10, CIFAR-100, and SVHN.
- 33.5% to 29.0% top-1 error was achieved on ILSVRC 2012 with GoogLeNet, while CIFAR-10 error fell from 8.22% to 6.93%.
2. Related work
The paper situates SamplePairing among established augmentation and synthetic-sample methods, emphasizing that it pairs images directly and applies pairing across the entire dataset.
- Traditional image augmentation creates samples through flipping, distortion, noise addition, or random patch cropping.
- SamplePairing is complementary to dropout and batch normalization, which address overfitting through different mechanisms.
- SMOTE synthesizes feature-space samples for minority classes, whereas SamplePairing applies image pairing across the entire dataset.
- Wang and Perez use an augmentation network to pair images, while SamplePairing directly averages image intensities.
- Related independently developed methods also mix randomly selected samples and extend the idea beyond image classification to tasks including sound recognition and GANs.
3. Method – Data Augmentation by SamplePairing
SamplePairing randomly pairs training images, averages them pixelwise, and trains on the mixed image with the first image's label. Training begins and ends without pairing, with intermittent pairing during the middle phase.
- Data augmentation: SamplePairing synthesizes a new image by averaging the intensities of two randomly selected training images pixel by pixel.
- Data augmentation: For each epoch, images are presented in randomized order and paired with another training image selected using a random number.
- Data augmentation: The mixed image is trained with the first image's label; the second image's label is not used.
- Training behavior: Equal image weighting prevents zero training loss for differently labeled pairs and makes SamplePairing a strong regularizer.
- Training process: Training starts without SamplePairing, enables it after an initial period, intermittently disables it, and finishes with fine-tuning without pairing.
- Training process: The authors empirically found the described training process gave good accuracy, although other processes also improved performance.
4. Experiments
Experiments across multiple image-classification datasets evaluate SamplePairing’s effects on error rates, losses, training-set size, and overlay-image selection. The method improves validation accuracy while increasing training error and provides larger gains with fewer training samples.
- SamplePairing reduced validation classification error rates across all evaluated datasets, with reported reductions ranging from 3.1% to 28.8%.
- SamplePairing increased training error while reducing validation error after fine-tuning on CIFAR-10 and ILSVRC.Training error was about 50% for CIFAR-10, while validation error became much lower than without SamplePairing.
- SamplePairing increased final training loss but reduced validation loss on ILSVRC, without the later validation-loss increase observed for the baseline.
- Using non-training images also reduced error in most tested settings, but selecting overlay images from the training set produced more significant improvements.
- Selecting overlay images from the entire training set outperformed same-class selection, while different-superclass selection achieved comparable or slightly better accuracy.
5. Conclusion
SamplePairing is an easy-to-implement augmentation that mixes two randomly selected images before classification training, improving accuracy especially with limited training data. The paper reports empirical benefits but leaves theoretical explanations for future work.
- SamplePairing mixes two randomly picked images before they are fed into a classifier for training.The technique is presented as simple to implement and does not require modifying the classification network.
- SamplePairing gives significant classification-accuracy improvements, especially when the number of available training samples is limited.The paper identifies medical image classification as an example of a task with limited training data.
- The paper discusses only empirical evidence and does not provide strong theoretical explanations or proof for why SamplePairing improves generalization.Future work includes developing theoretical foundations and applying the technique to other machine-learning tasks.