论文标题
色度VAE：使用生成分类器缓解快捷方式学习
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers
论文作者
论文摘要
深层神经网络容易受到快捷方式学习的影响，使用简单的功能来实现低训练损失而不发现基本的语义结构。与先前的信念相反，我们表明，尽管激励恢复数据比判别方法更全面地表示数据，但仅生成模型就不足以防止快捷方式学习。但是，我们观察到快捷方式优先用最小信息编码，这一事实是生成模型可以利用减轻快捷方式学习。特别是，我们提出了Chroma-Vae，这是一种两管齐下的方法，最初对VAE分类器进行了训练，以将快捷方式隔离在一个小潜在子空间中，从而使辅助分类器可以接受互补的无快捷潜在潜在子空间的培训。除了证明色度VAE在基准和现实世界快捷方式学习任务上的功效外，我们的工作还强调了操纵生成分类器的潜在空间来隔离或解释特定相关性的潜力。
Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure. Contrary to prior belief, we show that generative models alone are not sufficient to prevent shortcut learning, despite an incentive to recover a more comprehensive representation of the data than discriminative approaches. However, we observe that shortcuts are preferentially encoded with minimal information, a fact that generative models can exploit to mitigate shortcut learning. In particular, we propose Chroma-VAE, a two-pronged approach where a VAE classifier is initially trained to isolate the shortcut in a small latent subspace, allowing a secondary classifier to be trained on the complementary, shortcut-free latent subspace. In addition to demonstrating the efficacy of Chroma-VAE on benchmark and real-world shortcut learning tasks, our work highlights the potential for manipulating the latent space of generative classifiers to isolate or interpret specific correlations.
