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To use people's random images for training, they would have to be manually annotated by a human (e.g. facial boxes, eyes, nose, mouth, ears drawn in). There ar
by arghwhat 7y ago
To use people's random images for training, they would have to be manually annotated by a human (e.g. facial boxes, eyes, nose, mouth, ears drawn in).
There are also diminishing returns from additional data if it does not bring vastly new scenarios to train against.
Access to raw data alone is rarely a problem, so I would personally doubt that Google would have much value in Google Photos for training.
- _wp_ 7y ago> To use people's random images for training, they would have to be manually annotated by a human (e.g. facial boxes, eyes, nose, mouth, ears drawn in). That's not true. There is a large and growing body of research on semi-supervised, self-supervised, and unsupervised learning that can take advantage of these unlabelled images.
- arghwhat 7y agoDifferent learning techniques have different applications. I do not believe those techniques are applicable to the hypothetical use-cases of this dataset. Perhaps semi-supervised could be utilized, which reduces the required annotation by some factor k, but still leaves it as a function of the dataset. Self-supervised basically replaces human annotation with machine annotation, making it only applicable to a small subset of tasks in which this is possible (e.g. you could train "guess time from picture" using EXIF timestamp). Unsupervised is only applicable to very specific tasks.