Chen Liu ([email protected])
Please kindly Star 
We provide a simple wrapper around PyTorch DataLoader to intentionally mess up the input/label correspondence.
In the majority of times, when we train a machine learning model, we pay extra attention to make sure the inputs and labels are correctly matched. In occasional situations, however, we may want the opposite to happen. One such possibility is, as outlined in the paper "Understanding deep learning requires rethinking generalization", we may want to corrupt the training set and intentionally overfit a model on random labels.
Despite careful search on the internet, we were unable to find existing open-source implementations to achieve this purpose. Therefore we designed our own method to achieve this purpose and provided it to those who may have a similar need.
train_loader = ... # define `train_loader` as you normally would
train_loader = CorruptedLabelDataLoader(train_loader)
for (x, y) in train_loader:
...
This repository currently only contains a single file, which itself contains a single class called CorruptedDataLoader. CorruptedDataLoader is a wrapper around a Pytorch DataLoader. The Dataloader may hold arbitrary datasets, while in the current implementation, we only support the following datasets:
torchvision.datasets.MNISTtorchvision.datasets.CIFAR10torchvision.datasets.CIFAR100torchvision.datasets.STL10
Meanwhile, it can be easily adapted to any custom dataset, as long as you know under what key the labels are stored.
To use, simply copy CorruptedDataLoader to an appropriate location in your codebase and modify as you need. Don't forget to give us a star if you use it and find it helpful.
To be added