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Controllable Image Generation With Composed Parallel Token Prediction
We find that our method can generalise to combinations of input conditions that lie outside the training data (e. g. more objects per image) in addition to offering an interpretable dimension of controllability via concept weighting.
Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation
Whilst face recognition applications are becoming increasingly prevalent within our daily lives, leading approaches in the field still suffer from performance bias to the detriment of some racial profiles within society.
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