Zero-Shot Natural Language Explanations

Natural Language Explanations (NLEs) interpret the decision-making process of a given model through textual sentences. Current NLEs suffer from a severe limitation; they are unfaithful to the model's actual reasoning process, as a separate textual decoder is explicitly trained to generate those explanations using annotated datasets for a specific task, leading them to reflect what annotators desire. In this work, we take the first step towards generating faithful NLEs for any visual classification model without any training data. Our approach models the relationship between class embeddings from the classifier of the vision model and their corresponding class names via a simple MLP which trains in seconds. After training, we can map any new text to the classifier space and measure its association with the visual features. We conduct experiments on 38 vision models, including both CNNs and Transformers. Our method outperforms supervised baselines on many metrics, while remaining comparable on others. In addition to NLEs, our method offers other advantages such as zero-shot image classification and fine-grained concept discovery, each outperforming baseline methods. Finally, we also show that our method achieves state-of-the-art results on zero-shot image captioning.