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This doesn't really seem like "Overfitting" as I understand the concept. This is more like training a model to do a specific task rather than a more general ta

This doesn't really seem like "Overfitting" as I understand the concept. This is more like training a model to do a specific task rather than a more general task. Overfitting would be if your model started to memorize the training data - which doesn't seem to be what they are talking about and doesn't seem like it would be very useful..

deleted 5y ago
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malux85 5y ago
This is not over-fitting, a fully overfit model is one that would only detect positives on the 5 or so frames that it was trained on, but this is clearly generalising well to unseen frames - many unseen frames - so yeah, like you say, it’s not overfit.
manojlds 5y ago
Calling these as overfit is like saying a microservice for users can add only the specific 10 users and no one else.
ghego1 5y ago
The way I see it, this is over fitting. They train the model on only five frames, and then detect all close frames. They say that with five frames they are able to get 500 new labelled frames. This means 100 new frames per each original frame. Because movies are at 24 frames per second, this in turn means that each original frame gives enough information for analyzing more or less 4 seconds of video on average. As they show in the clips in the post, the short clips do indeed portrait Batman in very similar positions, with similar shading, light, etc. The micro model is able to detect Batman because the frames are all very similar to one another. It is very likely that this micro model as is wouldn't be able to detect Batman in a completely different scene of the movie. So the model is indded over fitted, meaning that it is able to detect Batman in a very specific set of data. Of course over fitting can be done at different levels. They do not over fit to the point that they would be able to detect only the five original frames. The over fitting here stops when there is still scope for capturing new data with the over fitted model. The smart idea of the authors is then to use these micro models to generate a lot of labeled data and "stich" together the micro models, so that they end up with a much larger data to train on, and a much more general model.
- ALittleLight 5y ago
  I agree that is what the post describes and that it could be a useful process. I don't think "overfitting" describes that process though. Overfitting describes increasing your performance on the training set to the extent that your model performs worse on the data it is used on. If overfitting is happening here then it wouldn't be beneficial. There is no reason to prefer that your model be better on the training set if you are going to use it to collect batman images across a film. It would be better if your model wasn't overfit, if it performed better on your dataset, then it would collect more images.
elandau25 5y ago
Hi everyone, I wrote the article. I do consider this overfitting because we are training on these frames way more time than would be normally advised for the size of the training set such that the error is essentially zero for these frames. The model performs well in "out-of-sample" here but only out of sample that is semantically close to the original training set. Besides, overfitting is defined procedurally, not by how well it performs. You could have an overfit model that just happens to perform well on some stuff it was not trained on, that doesn't change the fact that the model was overfit.
- MillenialMan 5y ago
  Is that overfitting? I thought overfitting was explicitly when your model fits itself so well to the training data that its ability to generalise to your target environment begins to decline. The size of the training set and the performance on it don't really matter, all that matters is that performance in the specific environment you intend to deploy the model in starts dropping. In your case, you're training the models for a very narrow deployment target, and they remain competent. Couldn't you use this logic to say that AlphaGo is overfit because it can only play Go, not chess?
- elanning 5y ago
  As I understand it, overfitting is low bias, but high variance. It's perfectly fitting 5 linear data points with a complex polynomial, when the underlying function was a line. Thus the polynomial doesn't generalize well to more data points not in the training set. Your model seems to be fitting points in the training set and the evaluation set just fine. Of course if different batman's were in the evaluation set, it would suddenly be doing terrible, but you can pretty much do that to every machine learning model. It wouldn't fit a lot of underlying assumptions of statistics and machine learning, eg i.i.d and evaluation sets/training sets being from the same distribution. Your definition of overfitting thus seems more like transfer learning, in some sense.
  - elandau25 5y ago
    I see what you are saying, but in that context then you lose what most people's intuitive definition of overfitting is. If I train a model on one image as my train set and then change one random pixel and run that model on this eval set then your argument would be that this is not overfitting because you are performing well on the eval set you created the model for. My argument is that compared to models, as most people use them, micro-models are low bias and high variance, and thus overfit. That's why I set a distinction between a batman model and a batman micro-model.

deleted 5y ago

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malux85 5y ago

This is not over-fitting, a fully overfit model is one that would only detect positives on the 5 or so frames that it was trained on, but this is clearly generalising well to unseen frames - many unseen frames - so yeah, like you say, it’s not overfit.

manojlds 5y ago

Calling these as overfit is like saying a microservice for users can add only the specific 10 users and no one else.

ghego1 5y ago

The way I see it, this is over fitting. They train the model on only five frames, and then detect all close frames. They say that with five frames they are able to get 500 new labelled frames. This means 100 new frames per each original frame. Because movies are at 24 frames per second, this in turn means that each original frame gives enough information for analyzing more or less 4 seconds of video on average. As they show in the clips in the post, the short clips do indeed portrait Batman in very similar positions, with similar shading, light, etc. The micro model is able to detect Batman because the frames are all very similar to one another. It is very likely that this micro model as is wouldn't be able to detect Batman in a completely different scene of the movie. So the model is indded over fitted, meaning that it is able to detect Batman in a very specific set of data. Of course over fitting can be done at different levels. They do not over fit to the point that they would be able to detect only the five original frames. The over fitting here stops when there is still scope for capturing new data with the over fitted model. The smart idea of the authors is then to use these micro models to generate a lot of labeled data and "stich" together the micro models, so that they end up with a much larger data to train on, and a much more general model.

ALittleLight 5y ago
I agree that is what the post describes and that it could be a useful process. I don't think "overfitting" describes that process though. Overfitting describes increasing your performance on the training set to the extent that your model performs worse on the data it is used on. If overfitting is happening here then it wouldn't be beneficial. There is no reason to prefer that your model be better on the training set if you are going to use it to collect batman images across a film. It would be better if your model wasn't overfit, if it performed better on your dataset, then it would collect more images.

elandau25 5y ago

Hi everyone, I wrote the article. I do consider this overfitting because we are training on these frames way more time than would be normally advised for the size of the training set such that the error is essentially zero for these frames. The model performs well in "out-of-sample" here but only out of sample that is semantically close to the original training set. Besides, overfitting is defined procedurally, not by how well it performs. You could have an overfit model that just happens to perform well on some stuff it was not trained on, that doesn't change the fact that the model was overfit.

MillenialMan 5y ago
Is that overfitting? I thought overfitting was explicitly when your model fits itself so well to the training data that its ability to generalise to your target environment begins to decline. The size of the training set and the performance on it don't really matter, all that matters is that performance in the specific environment you intend to deploy the model in starts dropping. In your case, you're training the models for a very narrow deployment target, and they remain competent. Couldn't you use this logic to say that AlphaGo is overfit because it can only play Go, not chess?
elanning 5y ago
As I understand it, overfitting is low bias, but high variance. It's perfectly fitting 5 linear data points with a complex polynomial, when the underlying function was a line. Thus the polynomial doesn't generalize well to more data points not in the training set. Your model seems to be fitting points in the training set and the evaluation set just fine. Of course if different batman's were in the evaluation set, it would suddenly be doing terrible, but you can pretty much do that to every machine learning model. It wouldn't fit a lot of underlying assumptions of statistics and machine learning, eg i.i.d and evaluation sets/training sets being from the same distribution. Your definition of overfitting thus seems more like transfer learning, in some sense.
- elandau25 5y ago
  I see what you are saying, but in that context then you lose what most people's intuitive definition of overfitting is. If I train a model on one image as my train set and then change one random pixel and run that model on this eval set then your argument would be that this is not overfitting because you are performing well on the eval set you created the model for. My argument is that compared to models, as most people use them, micro-models are low bias and high variance, and thus overfit. That's why I set a distinction between a batman model and a batman micro-model.