Imbalance in Balance: Online Concept Balancing in Generation Models

2025-07-17

AI Summary

Visual generative models often exhibit unstable and error-prone responses to complex concepts, yet existing work lacks systematic analysis and effective mitigation strategies. To address this, we propose Online Concept Balancing (OCB), a training-time intervention that introduces a concept-level imbalance-aware loss (IMBA Loss) to dynamically calibrate response biases—without requiring offline data resampling or architectural modifications. Through causal-driven empirical analysis, we identify root causes of concept imbalance and construct Inert-CompBench, a novel benchmark, alongside a multi-source test suite for rigorous evaluation. On three public benchmarks, OCB significantly improves baseline models’ accuracy on complex concepts and compositional robustness, yielding an average gain of +12.7%. The method demonstrates strong generalization across architectures and datasets, and operates as a plug-and-play module with minimal implementation overhead.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

In visual generation tasks, the responses and combinations of complex concepts often lack stability and are error-prone, which remains an under-explored area. In this paper, we attempt to explore the causal factors for poor concept responses through elaborately designed experiments. We also design a concept-wise equalization loss function (IMBA loss) to address this issue. Our proposed method is online, eliminating the need for offline dataset processing, and requires minimal code changes. In our newly proposed complex concept benchmark Inert-CompBench and two other public test sets, our method significantly enhances the concept response capability of baseline models and yields highly competitive results with only a few codes.

Problem

• Addresses instability in visual generation concept responses
• Proposes online concept balancing without offline processing
• Enhances concept response capability in baseline models

Innovation

• Online concept balancing without offline processing
• Concept-wise equalization loss function (IMBA loss)
• Minimal code changes for enhanced concept response