51–60 of 75 posts
Somewhat related to this: What methods do people normally use to measure the quality of a dataset? For example, if there are 50 datasets of historical weather data how can I determine which one is garbage?
I would say it's garbage if there's a paper like this: https://arxiv.org/abs/1902.01007
So, like humans, training an AI takes lots of curated data (education, journalism, political savvy and avoidance of bias or corruption). It took a very long time to get that corpus ready and available for humanity (say 10,000 years). Now we have exploded that corpus to include everything anyone says online, and we are worried that the lack of curation means we cannot be sure what the models will come back with. Its r…
Somewhat related to this: What methods do people normally use to measure the quality of a dataset? For example, if there are 50 datasets of historical weather data how can I determine which one is garbage?
When someone used the word “overfitting” I usually take that to mean that a model has begun to enter the phase where further improvement is leading to lower generalization. In fact, as far as I can tell, we are not overfitting in this sense. When I have seen papers examine whether progress on, let’s say, imagenet, actually generalizes to other categorization datasets the answer is yes. What we have been seeing is tha…
I disagree: some "collective overfitting" happens when everyone evaluates on the same dataset (and often, the same test set). There's a neat set of papers by Recht et al. showing results are slightly overfit to the test partitions of ImageNet and CIFAR-10: rotating examples between the train and test partitions causes systems to perform up to 10-15% worse. https://arxiv.org/abs/1806.00451 https://arxiv.org/abs/1902.1…
Situation A: Models are slightly overfit to some portions of the test set. But the following holds
IF PerformanceOnBenchmark(Model A) > PerformanceOnBenchmark(Model B) Then PerformanceOnSimilarDaset(Model A) > PerformanceOnSimilarDataset(Model B)
Therefore progress on the benchmark is predictive of progress in general.
Situation B: The relation does not hold, and therefore progress on the benchmark does not predict general progress. This almost always happen if you train a deep neural network long enough: train performance goes up, but test performance goes down.
If you look at figure 2 of the first paper you sent, you will note that it shows we are in situation A and not situation B.
Situation A overfitting = diminishing returns on improvements on benchmark, but the benchmark is still useful. Situation B overfitting = the benchmark is now useless
A little confused: Are these big "dominant" institutions charging for this data? No, the spend a lot of resources putting them together and give them away free. Are they preventing others from giving away data? No, but it costs a lot and they bear that cost. Are they forcing smaller institutions to use their data? No. Its just a free resource they offer. Do they get the grants themselves because they have some kind o…
> So they collect this data, vet it, propose their use for benchmarks and give it away free. What is the complaint? It's well known that neural networks can easily inherit biases from their training data. It's also well known that datasets generated by western universities are widely used in training and evaluating neural networks. If my training set is full of pictures of Stanford CS undergraduates, I could end up w…
I don't think this excuse is like the others. If the model doesn't work well because they used bias data, it is the job of the people making the model to find better data (or manipulate the training process to overweight some data and attempt to counteract the bias).
I think the burden of responsibility has to be on the people who make models or put models into products to make sure the model is a good fit for the problem it is solving.
Earlier quoted context omitted.
Is the term "browbeat" fair here? I don't think they're making calls and saying "Oh, nice paper, but I notice you used this other dataset..." No, they're putting out good quality datasets that people want to use. If that earns them a citation, good for them. Its the least I can do for helping me test my algo.
It depends, feedback is rarely as polite as "I noticed you used this other dataset". The feedback would probably look like. - "Nice paper, however the results are not relevant to current research due to the use of X dataset rather than Y or Z datasets score 2/5 do not accept." - "Nice paper, however the results are of unknown quality due to the use of X dataset 3/5 recommend poster track". In fact I'd generally say t…
If you are establishing new baselines using those same models on your non-standard dataset, then one would expect you to put in a good amount of effort to finetune all the knobs to get a reasonable result. If the authors put are able to put in that much effort, then that kind of feedback is definitely unreasonable.
Earlier quoted context omitted.
I disagree: some "collective overfitting" happens when everyone evaluates on the same dataset (and often, the same test set). There's a neat set of papers by Recht et al. showing results are slightly overfit to the test partitions of ImageNet and CIFAR-10: rotating examples between the train and test partitions causes systems to perform up to 10-15% worse. https://arxiv.org/abs/1806.00451 https://arxiv.org/abs/1902.1…
I think it's super important to separate the following two situations, both of which I suppose are fair to call overfitting Situation A: Models are slightly overfit to some portions of the test set. But the following holds IF PerformanceOnBenchmark(Model A) > PerformanceOnBenchmark(Model B) Then PerformanceOnSimilarDaset(Model A) > PerformanceOnSimilarDataset(Model B) Therefore progress on the benchmark is predictive…
This is a problem that is more common for classification problems, I think. Generative and self-supervised models (trained with augmentation) tend to just get better forever (with some asymptote) because memorization isn't a viable strategy.
I personally think image classification is mostly a silly problem to judge new algos on as a result, and leads to all kinds of nonsense as people try to extrapolate meaning from new results.
Once solved, these no longer are research areas. Getting X score on a Y validation set means you get your s/w drivers license.
I'll have to read the paper to see what exactly it says on this but my knowledge of NLP benchmark datasets is exactly the opposite: the majority are simply no use for measuring the capabilities they're supposed to be measuring. For example, natural language _understanding_ datasets are typically created as multiple-choice questionnaires, so that, a) there is already a baseline accuracy that a system can "hit" just by chance (but which is almost never noted, or compared against, in papers) and b) a system that's good at classification can beat the benchmarks black and blue without doing any "understanding". And this is, indeed, what's been going on for quite a while now with large language models that take all the trophies and are still dumb as bricks.
To make matters worse, NLP also doesn't have any good metrcis of performance. Stuff like the BLEU scores are just laughably inadequate. NLP is all bad metrics over bad benchmarks. And NLP results are much harder to just "eyball" than machine vision results (and the models are much harder to interpret than machine vision models where you can at least visualise the activations and see... something). I think NLP is much, much worse than machine vision.