BigGANs in Data Augmentation

Key Takeaways

[Music] this video will present a study on using data generated from the big Gann model for the purpose of data augmentation big ganzar one of the state of the Arts in generative adversarial image synthesis the images on this slide are completely generated from a big an model the dog the mountain the butterfly and the cheeseburger are all completely imagined up by this generative adversarial Network model so the idea is can you replace or augment the original image net dataset by adding the data generated by the scan it seems like it would work because you're able to generate novel dog images novel cheeseburger images surely it's intuitive and it should work that if you add these images to the classifier it'll learn a stronger decision boundary so the first test in this study is to replace the image net data with big Gann generated image net data and the so the different levels across this table are different values for the truncation trick which is a sampling technique used and began specifically where they replace different values along the z vector if they fall outside the truncation range and this is a trade-off between quality and diversity so they find is with the higher values of the truncation which have higher diversity of lower quality they get the best result by training with training and image net classifier on but the most interesting part about this study is that the error between the model train with image metadata only and the model train with big and generated data is way higher you see the best model gets 57% top one compared to 26% and 34% top five compared to 7% so even though they might look realistic in terms of from a classifiers perspective the big an generated data isn't very useful so this plot shows the performance by class because across the 1,000 images the accuracy of using the image net versus big and generated data varies and these images of squirrel monkey and fox these are actually two of the classes that perform better with the big and generated data than the image net data but only a marginal improvement whereas some other classes are completely tanked by this method so one other idea they tested was combining the image net data and the big Gantt data for training and misra did actually result positively with the 3% relative improvement so not plus three percent accuracy but three percent better than the original result and this is a marginal improvement but it did come at the cost of one and a half times the training time which is pretty big cost so this makes you question the evaluate evaluation metrics used to evaluate games inception score and the inception distance even though they're really high for the big game model they don't perform well for this downstream task of data augmentation so thanks for watching this video on using big an generated data for the task of data augmentation and improving image net classification models so thanks for watching again please subscribe to Henry AI labs for more deep learning video videos also the paper link for this study is in the description [Music]