4 ms·
The key is the scale of the dataset. Both the points you mention become irrelevant for a large dataset because 1) The chance that a significant percentage of t
by alsodumb 4y ago
The key is the scale of the dataset. Both the points you mention become irrelevant for a large dataset because
1) The chance that a significant percentage of the images become unavailable is low. Also, training on such a big dataset means your model generalizes well and is usually robust.
2) Again, you would need to inject adversarial/malicious images to a significant number of those links in the dataset for it to have actual impact on trained model. Again, unlikely.
- abeppu 4y agoFor point 1 ... it depends on the timescale. In the fullness of time, surely a significant portion of images will be unavailable. From the perspective of allowing other researchers to work from the "same" baseline "today", this is likely good enough. In a generation from now, if someone wants to reproduce results from some landmark model trained against this dataset, we'd have problems. In other fields where people publish or share their datasets, would this be considered sufficient? For point 2, I think it's possible that for some narrow topics, some domains have a significant share of images. I think these can affect the model, which is in part why they give special attention to watermarking. Suppose instead of merely watermarking images, for every image on my large collegiate track and field website I make sure someone is wearing a garment with a visible Nike swoosh. Can I skew the model towards associating Nike with the sport? I think this kind of thing may be achievable for niche areas.
- deleted 4y ago[deleted]