3 ms·
People are studying ways to train accurate labels with noisy labels (see the paragraph in this article: https://tmabraham.github.io/blog/noisy_imagenette#Prior-
by tmabraham 6y ago
People are studying ways to train accurate labels with noisy labels (see the paragraph in this article: https://tmabraham.github.io/blog/noisy_imagenette#Prior-research-on-noisy-labels https://tmabraham.github.io/blog/noisy_imagenette#Prior-rese...)
Also, technically accuracy as a metric is robust to noise (https://arxiv.org/abs/2012.04193 https://arxiv.org/abs/2012.04193). That means that a model with the highest accuracy on a noisy dataset will likely be the best model on the clean dataset. So these noisy datasets can still be very useful for the development of deep learning models. In fact if you look at the tradeoffs between getting larger datasets that have noisy labels vs. smaller datasets will clean labels (since good annotation is expensive!), the noisy large-scale dataset will probably be more useful.
- black_puppydog 6y agoI just skimmed both sources you list and Ctrl-F "bias" seems to indicate they assume unbiased noise. Which might be okay depending on the application, but for many data collection processes will not be okay. Imagine if one group of people's wealth is systematically under-estimated (aka "measured") when training an insurance policy AI. The algorithm will then correctly learn the bias in the dataset. None of this is magic. Data collection is hard. Spotting your own biases before they make it into your dataset is a blind-spot exercise.
- skrebbel 6y agoI'm not trying to refute the problem of biased AI data, but I do want to understand it better. You mention an "insurance policy AI". The rest of this thread seems to be about image recognition. What's an insurance policy AI and how does it work? Is it a thing or a hypothetical future thing? Can bias effects have equally bad effects in image recognition? I know about the story of the photo software that categorized a man's holiday photos under "gorillas" because it was trained only on white people. This is terribly insulting, but less terrible than an insurance company unfairly overcharging you or refusing you service. I guess what I'm saying is I can't come up with biased image recognition AIs having unfair outcomes that actually affect people deeply, and I'm likely missing lots of terrible examples, and I'd love if someone can educate me. EDIT: before people erupt in outrage, I'm not saying that a computer telling you that you're a gorilla isn't terrible, but in the end it's an indictment of the software, not you.
- whynaut 6y agoIf that software can’t distinguish a black man from a gorilla, i’m sure it’ll have trouble distinguishing two black men at least some of the time.
- lanstin 6y agoPulling people for extra checks in a security screening use case (from an image get an emotional state rating). Not hitting pedestrians in a self driving car. Also a lot of image scanning applications are being sold as “upload a photo of a prospective employee and a get a trust worthiness rating”.
- deleted 6y ago[deleted]
- 3pt14159 6y agoThis is akin to how humans learn too, so I'm not surprised that it is the case. Little kids learn how to listen to verbal commands even when, say, a flight is going by overhead. Most of what we learn is in a bit of a mush of data, and I think this is a feature not a bug, since it allows the models to be more robust. It may be one of the precursors to intelligence: The ability to cognitively filter out unnecessary sensory stimulus in order to focus perception to the object or subject of a creature's attention.
- IshKebab 6y ago> That means that a model with the highest accuracy on a noisy dataset will likely be the best model on the clean dataset. That's exactly the opposite of what this article says.
- alex_hirner 6y agoExactly. The observation that a less faulty model is likely less accurate on a noisy validation set than a more faulty model, doesn't change the fact that there must be faulty models with higher accuracy than a perfect model on a noisy validation set.