DALL·E 2 pre-training mitigations

DALL·E 2 pre-training mitigations | OpenAI

June 28, 2022

DALL·E 2 pre-training mitigations

DALL·E

Loading…

Share

In order to share the magic of DALL·E 2⁠ with a broad audience, we needed to reduce the risks associated with powerful image generation models. To this end, we put various guardrails⁠ in place to prevent generated images from violating our content policy⁠.

This post focuses on pre-training mitigations, a subset of these guardrails which directly modify the data that DALL·E 2 learns from. In particular, DALL·E 2 is trained on hundreds of millions of captioned images from the internet, and we remove and reweight some of these images to change what the model learns.

This post is organized in three sections, each describing a different pre-training mitigation:

• In the first section, we describe how we filtered out violent and sexual images from DALL·E 2’s training dataset. Without this mitigation, the model would learn to produce graphic or explicit images when prompted for them, and might even return such images unintentionally in response to seemingly innocuous prompts.
• In the second section, we find that filtering training data can amplify biases, and describe our technique to mitigate this effect. For example, without this mitigation, we noticed that models trained on filtered data sometimes generated more images depicting men and fewer images depicting women compared to models trained on the original dataset.
• In the final section, we turn to the issue of memorization, finding that models like DALL·E 2 can sometimes reproduce images they were trained on rather than creating novel images. In practice, we found that this image regurgitation is caused by images that are replicated many times in the dataset, and mitigate the issue by removing images that are visually similar to other images in the dataset.

Reducing graphic and explicit training data

Since training data shapes the capabilities of any learned model, data filtering is a powerful tool for limiting undesirable model capabilities. We applied this approach to two categories—images depicting graphic violence and sexual content—by using classifiers to filter images in these categories out of the dataset before training DALL·E 2. We trained these image classifiers in-house and are continuing to study the effects of dataset filtering on our trained model.

To train our image classifiers, we reused an approach that we had previously employed to filter training data for GLIDE⁠. The basic steps to this approach are as follows: first, we create a specification for the image categories we would like to label; second, we gather a few hundred positive and negative examples for each category; third, we use an active learning procedure to gather more data and improve the precision/recall trade-off; and finally, we run the resulting classifier on the entire dataset with a conservative classification threshold to favor recall over precision. To set these thresholds, we prioritized filtering out all of the bad data over leaving in all of the good data. This is because we can always fine-tune our model with more data later to teach it new things, but it’s much harder to make the model forget something that it has already learned.

Loading...

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.

To verify the effectiveness of our data filters, we trained two GLIDE models with the same hyperparameters: one on unfiltered data, and one on the dataset after filtering. We refer to the former model as the unfiltered model, and the latter as the filtered model. As expected, we found that the filtered model generally produced less explicit or graphic content in response to requests for this kind of content. However, we also found an unexpected side-effect of data filtering: it created or amplified the model’s biases towards certain demographics.

Loading...

Fixing bias introduced by data filters

Generative models attempt to match the distribution of their training data, including any biases therein. As a result, filtering the training data has the potential to create or amplify biases in downstream models. In general, fixing biases in the original dataset is a difficult sociotechnical task that we continue to study, and is beyond the scope of this post. The problem we address here is the amplification of biases caused specifically by data filtering itself. With our approach, we aim to prevent the filtered model from being more biased than the unfiltered model, essentially reducing the distribution shift caused by data filtering.

As a concrete example of bias amplification due to filtering, consider the prompt “a ceo”. When our unfiltered model generated images for this prompt, it tended to produce more images of men than women, and we expect that most…