  Computer vision models learn to perform a task by capturing relevant
statistics from training data. It has been shown that models learn spurious
age, gender, and race correlations when trained for seemingly unrelated tasks
like activity recognition or image captioning. Various mitigation techniques
have been presented to prevent models from utilizing or learning such biases.
However, there has been little systematic comparison between these techniques.
We design a simple but surprisingly effective visual recognition benchmark for
studying bias mitigation. Using this benchmark, we provide a thorough analysis
of a wide range of techniques. We highlight the shortcomings of popular
adversarial training approaches for bias mitigation, propose a simple but
similarly effective alternative to the inference-time Reducing Bias
Amplification method of Zhao et al., and design a domain-independent training
technique that outperforms all other methods. Finally, we validate our findings
on the attribute classification task in the CelebA dataset, where attribute
presence is known to be correlated with the gender of people in the image, and
demonstrate that the proposed technique is effective at mitigating real-world
gender bias.
