Imbalance in Balance: Online Concept Balancing in Generation Models

The paper 'Imbalance in Balance: Online Concept Balancing in Generation Models' highlights a critical issue in visual generation tasks, where responses to complex concepts often lack stability and are prone to errors. To tackle this, the authors introduce the IMBA loss function, designed to equalize concept responses effectively. This method operates online, streamlining the process by removing the need for offline dataset processing and requiring minimal code alterations. The introduction of the Inert-CompBench benchmark further validates the effectiveness of the IMBA loss, showing significant improvements in the response capabilities of baseline models. The results are promising, indicating that this approach can lead to more reliable AI-generated content. The authors have made a few codes available on GitHub, encouraging further exploration and application of their method in the AI community.