  Overfitting & underfitting and stable training are an important challenges in
machine learning. Current approaches for these issues are mixup, SamplePairing
and BC learning. In our work, we state the hypothesis that mixing many images
together can be more effective than just two. Batchboost pipeline has three
stages: (a) pairing: method of selecting two samples. (b) mixing: how to create
a new one from two samples. (c) feeding: combining mixed samples with new ones
from dataset into batch (with ratio $\gamma$). Note that sample that appears in
our batch propagates with subsequent iterations with less and less importance
until the end of training. Pairing stage calculates the error per sample, sorts
the samples and pairs with strategy: hardest with easiest one, than mixing
stage merges two samples using mixup, $x_1 + (1-\lambda)x_2$. Finally, feeding
stage combines new samples with mixed by ratio 1:1. Batchboost has 0.5-3%
better accuracy than the current state-of-the-art mixup regularization on
CIFAR-10 & Fashion-MNIST. Our method is slightly better than SamplePairing
technique on small datasets (up to 5%). Batchboost provides stable training on
not tuned parameters (like weight decay), thus its a good method to test
performance of different architectures. Source code is at:
https://github.com/maciejczyzewski/batchboost
