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Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks

2024-06-10 07:52:29
Victor Boutin, Rishav Mukherji, Aditya Agrawal, Sabine Muzellec, Thomas Fel, Thomas Serre, Rufin VanRullen


Humans can effortlessly draw new categories from a single exemplar, a feat that has long posed a challenge for generative models. However, this gap has started to close with recent advances in diffusion models. This one-shot drawing task requires powerful inductive biases that have not been systematically investigated. Here, we study how different inductive biases shape the latent space of Latent Diffusion Models (LDMs). Along with standard LDM regularizers (KL and vector quantization), we explore supervised regularizations (including classification and prototype-based representation) and contrastive inductive biases (using SimCLR and redundancy reduction objectives). We demonstrate that LDMs with redundancy reduction and prototype-based regularizations produce near-human-like drawings (regarding both samples' recognizability and originality) -- better mimicking human perception (as evaluated psychophysically). Overall, our results suggest that the gap between humans and machines in one-shot drawings is almost closed.

Abstract (translated)

人类可以轻松地从单个示例中绘制出新的类别,这一壮举长期以来一直是生成模型的挑战。然而,随着最近扩散模型的进步,这一差距开始缩小。这个一次性绘图任务需要强大的归纳偏见,这些偏见尚未系统地研究。在这里,我们研究了不同归纳偏见如何影响Latent Diffusion Models (LDMs)的潜在空间。与标准LDM正则化(KL和向量量化)相结合,我们探讨了监督正则化(包括分类和基于原型的表示)以及对比式归纳偏见(使用SimCLR和冗余消除目标)。我们证明了具有冗余减少和基于原型的正则化的LDMs产生接近人类水平的绘图(关于样本的可识别性和原创性)--更好地模拟人类感知(根据心理物理学评价)。总体而言,我们的结果表明,人类和机器在一次性绘图上的差距几乎已经消失。



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