How Did Google Researchers beat ImageNet while using fewer resources?

How Did Google Researchers beat ImageNet while using fewer resources?

Image Classification algorithms have been getting better mostly by using more resources (data, computing power, and time). The best algorithms use an extra 3.5 Billion labeled images. This paper, written by Qizhe Xie et al bucks the trend. It is able to beat the current algorithms while using only an extra 300 M unlabeled images (both algorithms use the same labeled Dataset of images

Image Classification algorithms have been getting better mostly by using more resources (data, computing power, and time). The best algorithms use an extra 3.5 Billion labeled images. **This paper, written by Qizhe Xie et al bucks the trend. It is able to beat the current algorithms while using _only _an extra *300 M unlabeled images *(both algorithms use the same labeled Dataset of images, on which we have the extra 3.5 B and 300 M images respectively). *This means that the new method uses **12 times *lesser images, while also not requiring those images to be labeled. Therefore it is many, many times cheaper while giving better results.

An illustration from the paper showing how they trained their models

On the surface, their approach seems to be standard SSL. The key to their superior performance lies in the steps taken before and during training. The unlabeled student model is larger than the teacher. The team also injects different types of noise into the data and models, ensuring that each student learns on more types of data distributions than their teachers. This combined with the Iterative Training is especially powerful since this makes use of the constantly improving teachers.

supervised-learning research image-classification machine-learning-ai machine-learning

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