While this unstructured exploration has proven successful in numerous tasks, it can be suboptimal for overactuated systems. When multiple actuators, such as motors or muscles, drive behavior, uncorrelated perturbations risk diminishing each other's effect, or modifying the behavior in a task-irrelevant way. While solutions to introduce time correlation across action perturbations exist, introducing correlation across actuators has been largely ignored.

Gender Ethnicity Adjective Profession woman man non-binary (person) African-American American Indian Asian Black Caucasian East Asian First Nations Hispanic Indigenous American Latino Latinx Native American Multiracial Pacific Islander South Asian Southeast Asian White Male-leaning: ambitious assertive confident decisive determined intelligent outspoken self-confident stubborn unreasonable committed Female-leaning: supportive sensitive emotional gentle honest modest compassionate considerate pleasant accountant aerospace engineer aide air conditioning installer architect author baker bartender career counselor carpenter carpet installer cashier CEO childcare worker civil engineer claims appraiser cleaner clergy clerk coach community manager compliance officer computer programmer computer support specialist computer systems analyst cook correctional officer courier credit counselor customer service rep. T able 4: A list of the social attributes (gender and ethnicity) and target attributes. All "professions" prompts specify a profession value. Statistically significant results are bolded. "Latinx" is the most frequently appearing ethnicity term and "woman" is the most frequent gender word.

Under both covariate and domain shift, models regularized with our proposed term showed marked increased robustness.

Table 7: We randomly select 100 samples for each fine-tuning. CelebA-HQ [27], we select 10 people, below are their id-number in dataset.Dataset fine-tuning Classes ImageNet [26] "wearing glasses" may be expressed with lower Higher α expresses more editing. If using A/B training, the images become "colorful castle" and emphasize the "laptop". We report three results for each input image. There are some mistaken cases, such as the "teddybear" But fine-tuning would fix the incompatible.

The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Training (CLIP).

Counterfactual generation lies at the core of various machine learning tasks, including image translation and controllable text generation.

Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems.