dimensionality-driven-learning
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Train on different dataset
Hi @xingjunm ,
I read your paper and its really intuitive. Thank you for sharing the code. Is it possible to use this code to train a different dataset than you mentioned in your repo. My dataset contains images with noisy multi-label tags and I'd like to see how dimensionality driven learning can improve performance compare to conventional classification models.
Thanks, Bhadresh
Hi bhadresh74, thanks for your interest. You can wrap up your new dataset in the datasets.py, load the data the same way as in train_models.py, you can try with a new CNN model (in models.py) or use the resnet base like in the current CIFAR-100 model.