用于生成器一致分类的联合流匹配
Joint Flow Matching for Generator-Consistent Classification
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中文总结 AI 辅助
研究提出联合流匹配（JFM）训练框架，解决标准流匹配在条件推理上的问题，通过为变量分配相反角色产生一致联合分布，用于联合分类和生成，在条件数据集验证，有竞争力准确率且无需事后校准，能生成与分类器一致的图像。
中文摘要 AI 辅助
我们引入了联合流匹配（JFM），这是一种用于多个变量上连续归一化流的训练框架。标准流匹配同时将变量从噪声传输到数据，没有为从共享联合模型进行正向和反向条件推理提供自然机制。JFM通过在时间端点为每个变量分配相反的角色来解决此问题。我们证明JFM产生一致的联合分布，其中正向或反向积分是同一联合的条件。我们在联合分类和生成的背景下探索这种一致性，作为判别-生成模型可解释性的基础。我们在条件数据集上验证JFM，产生具有竞争力的准确率，置信度得分无需事后校准，且生成与分类器一致的图像。
英文摘要
We introduce Joint Flow Matching (JFM), a training framework for continuous normalising flows over multiple variables. Standard flow matching transports variables from noise to data simultaneously, offering no natural mechanism for forward and reverse conditional inference from a shared joint model. JFM resolves this by assigning opposite roles to each variable at the temporal endpoints. We prove that JFM produces a consistent joint distribution where that forward or reverse integration are conditionals of the same joint. We explore this consistency in the context of joint classification and generation as the basis for interpretability in discriminative-generative models. We validate JFM on conditional datasets producing competitive accuracy with inherently well-calibrated confidence scores without post-hoc calibration, and classifier-consistent image generation.
