DALL·E 2 pre-training mitigations

During the active learning phase, we iteratively improved our classifiers by gathering human labels for potentially difficult or misclassified images. Notably, we used two active learning techniques to choose images from our dataset (which contains hundreds of millions of unlabeled images) to present to humans for labeling. First, to reduce our classifier’s false positive rate (i.e., the frequency with which it misclassifies a benign image as violent or sexual), we assigned human labels to images that the current model classified as positive. For this step to work well, we tuned our classification threshold for nearly 100% recall but a high false-positive rate; this way, our labelers were mostly labeling truly negative cases. While this technique helps to reduce false positives and reduces the need for labelers to look at potentially harmful images, it does not help find more positive cases that the model is currently missing.

To reduce our classifier’s false negative rate, we employed a second active learning technique: nearest neighbor search. In particular, we ran many-fold cross-validation to find positive samples in our current labeled dataset which the model tended to misclassify as negative (to do this, we literally trained hundreds of versions of the classifier with different train-validation splits). We then scanned our large collection of unlabeled images for nearest neighbors of these samples in a perceptual feature space, and assigned human labels to the discovered images. Thanks to our compute infrastructure, it was trivial to scale up both classifier training and nearest neighbor search to many GPUs, allowing the active learning step to take place over a number of minutes rather than hours or days.

We are continuing to investigate remaining biases in DALL·E 2, in part through larger evaluations of the model’s behavior and investigations of how filtering impacted bias and capability development.