Revisiting Unreasonable Effectiveness of Data in Deep Learning Era · HackerPapers discussions

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era — mentioned by godelski

Mon, 30 Aug 2021 19:51:46 GMT

Datasets are actually pretty hard to create. You can see several papers specifically studying ImageNet[0] including some on fairness and how labels matter. There's also Google's famous private JFT-300M dataset[1]. JFT was specifically made with heavy tails in the distribution to better help study these areas, which is specifically the problem we're interested with here and one that is not solved in ML. Even with more uniform datasets like CIFAR there are still many features that are noisy in the latent space. This is often one of the issues with doing facial recognition and why there's issues…

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Revisiting Unreasonable Effectiveness of Data in Deep Learning Era — mentioned by tedd4u

Wed, 16 Jan 2019 03:38:47 GMT

In image recognition, Google asserted in 2017 that they were unable to find decreasing returns -- "Performance increases logarithmically based on volume of training data." Maybe for supply-chain and regression models there is a limit but it seems in deep neural nets maybe there's a different answer. Blog: https://ai.googleblog.com/2017/07/revisiting-unreasonable-ef... Paper: https://arxiv.org/abs/1707.02968

Paper and discussion history. A mention is not an endorsement.

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era — HN submission

Sun, 23 Jul 2017 06:50:08 GMT

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era — HN submission

Mon, 17 Jul 2017 16:28:23 GMT

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era — HN submission

Wed, 12 Jul 2017 06:50:14 GMT

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.