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Visual Learning and Recognition: Difference between revisions
Visual Learning and Recognition (view source)
Revision as of 14:32, 11 September 2020
, 11 September 2020→Overcoming Datset Bias
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===Overcoming Datset Bias=== | ===Overcoming Datset Bias=== | ||
Mixing datasets | Mixing datasets | ||
;Selection bias | |||
In general, automatically gathered images do better. | |||
You can also collect data from multiple sources (multiple search engines across multiple countries) | |||
or collect unannotated images and label them via crowd-sourcing. | |||
;Capture bias | |||
To overcome the bias of professional photographs: | |||
Apply data augmentations: flipping images, jittering (small affine transformations), random crops. | |||
;Negative set bias | |||
Add negatives from other datasets. | |||
Mine hard negatives from other datasets using standard algorithms. | |||
==Data-driven Methods in Vision== | ==Data-driven Methods in Vision== | ||