When training a Deep learning model on multiple datasets, is it better concatenating all datasets and shuffling it, or train datasets sequentially?
So lets say that I Have datasets A, B, and C to train a model. My current solution take batches randomly from A, then from B, then from C. I wonder if concatenating all datasets and shuffling so that training would be more random would improve results

As you pointed out in your comment, the samples in the datasets are drawn from slightly different &quot;distributions&quot; (e.g., real vs synthetic images). In this case, it is better to randomly sample points from all datasets for each batch, rather than going sequentially through the different datasets.
