Affinity Diversification

Affinity diversification focuses on generating diverse outputs from models or systems, aiming to improve robustness, representation, and performance by mitigating biases and exploiting multiple perspectives. Current research explores this through techniques like mixture-of-experts models, uncertainty inference, and contrastive learning, often applied within frameworks such as diffusion probabilistic models or constraint-based optimization. This approach is proving valuable in various applications, including improving the accuracy and fairness of image analysis, enhancing the diversity of search results and recommendations, and strengthening software security against code-reuse attacks.