Renderers are Good Zero-Shot Representation Learners: Exploring Diffusion Latents for Metric Learning

• URL: http://arxiv.org/abs/2306.10721v1
• Date: Mon, 19 Jun 2023 06:41:44 GMT
• Title: Renderers are Good Zero-Shot Representation Learners: Exploring Diffusion Latents for Metric Learning
• Authors: Michael Tang, David Shustin
• Abstract summary: We use retrieval as a proxy for measuring the metric learning properties of the latent spaces of Shap-E. We find that Shap-E representations outperform those of the classical EfficientNet baseline representations zero-shot. These findings give preliminary indication that 3D-based rendering and generative models can yield useful representations for discriminative tasks in our innately 3D-native world.
• Score: 1.0152838128195467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Can the latent spaces of modern generative neural rendering models serve as representations for 3D-aware discriminative visual understanding tasks? We use retrieval as a proxy for measuring the metric learning properties of the latent spaces of Shap-E, including capturing view-independence and enabling the aggregation of scene representations from the representations of individual image views, and find that Shap-E representations outperform those of the classical EfficientNet baseline representations zero-shot, and is still competitive when both methods are trained using a contrative loss. These findings give preliminary indication that 3D-based rendering and generative models can yield useful representations for discriminative tasks in our innately 3D-native world. Our code is available at \url{https://github.com/michaelwilliamtang/golden-retriever}.

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