Utilization of classification latent space information for downstream reconstruction and generation is an intriguing and a relatively unexplored area. In general, discriminative representations are rich in class-specific features but are too sparse for reconstruction, whereas, in autoencoders the representations are dense but have limited indistinguishable class-specific features, making them less suitable for classification. In this work, we propose a discriminative modeling framework that employs manipulated supervised latent representations to reconstruct and generate new samples belonging to a given class. Unlike generative modeling approaches such as GANs and VAEs that aim to model the data manifold distribution, Representation based Generations (ReGene) directly represent the given data manifold in the classification space. Such supervised representations, under certain constraints, allow for reconstructions and controlled generations using an appropriate decoder without enforcing any prior distribution. Theoretically, given a class, we show that these representations when smartly manipulated using convex combinations retain the same class label. Furthermore, they also lead to the novel generation of visually realistic images. Extensive experiments on datasets of varying resolutions demonstrate that ReGene has higher classification accuracy than existing conditional generative models while being competitive in terms of FID.
翻译:为下游重建和生成利用分类潜层空间信息是一个令人着迷和相对未探索的领域。一般而言,有区别的表示形式在特定类别的特点方面很丰富,但对于重建而言却过于稀少,而在自动编码器中,这种表示形式是密集的,但具有有限的不可分的类别特点,因而不那么适合分类。在这项工作中,我们提议了一个有区别的模型框架,利用受操纵的受监督潜层表示形式来重建和生成属于某一类别的新样本。与GANs和VAEs等旨在模拟数据多重分布的基因化模型不同,基于代表性的代代(ReGene)直接代表分类空间中给定的数据组合。这种受监督的表示形式在某些限制下,允许利用适当的解密器进行重建和控制的代代代,而不执行先前的任何分配。从理论上讲,我们从一个类别来看,这些表示形式在使用convex组合进行精明的操纵时保留同一类别标签。此外,它们还导致新生成的视觉现实图像。关于不同决议数据集的广泛实验表明ReGene在具有竞争力的基因模型中具有更高的精确性。