Google AI    @GoogleAI     10/13/2021
Medical image classification models often pre-train on natural image datasets. Today, we present alternative approaches that use additional pre-training on medical images, along with metadata-based data augmentation, to significantly improve performance.
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Sebastian Raschka    @rasbt     10/4/2021
"PASS: An ImageNet replacement for self-supervised pretraining without humans" ( This datasets of 1.4 million images with CC-BY license, and fewer problematic images, seems like a good alternative to ImageNet for pre-training your next CV model.
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Andrei Bursuc    @abursuc     9/28/2021
Some interesting findings: - pre-training on PASS vs pre-training on IN-1k leads to performance in the same ball park for various downstream tasks - performance on human-centered downstream tasks, eg human dense pose prediction, is on par with IN-1k models 2/
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Arkaitz Zubiaga    @arkaitz     9/23/2021
馃 Performance of stance classification models drops over time due to changes in data. 馃挕 In this paper, we assess the extent to which performance drops and propose solutions to mitigate the decay. (w/ @rabab_alkhalifa @Elena_Kochkina)
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Prafulla Dhariwal    @prafdhar     6/9/2021
Exciting results on training a cascade of upsampling diffusion models. Adding data augmentation at the lower resolutions fixes the train-test gap during upsampling and allows to generate high quality natural images!
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Google AI
Google AI is focused on bringing the benefits of AI to everyone. In conducting and applying our research, we advance the state-of-the-art in many domains.

Sebastian Raschka
Author of the 'Python Machine Learning' book. Tweet about Python, deep learning research, open source. Asst Prof of Statistics @UWMadison. Opinions are my own.

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