LARGE: Latent-Based Regression through GAN Semantics
We propose a novel method for solving regression tasks using few-shot or weak supervision. At the core of our method is the fundamental observation that GANs are incredibly successful at encoding semantic information within their latent space, even in a completely unsupervised setting. For modern ge...
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Zusammenfassung: | We propose a novel method for solving regression tasks using few-shot or weak
supervision. At the core of our method is the fundamental observation that GANs
are incredibly successful at encoding semantic information within their latent
space, even in a completely unsupervised setting. For modern generative
frameworks, this semantic encoding manifests as smooth, linear directions which
affect image attributes in a disentangled manner. These directions have been
widely used in GAN-based image editing. We show that such directions are not
only linear, but that the magnitude of change induced on the respective
attribute is approximately linear with respect to the distance traveled along
them. By leveraging this observation, our method turns a pre-trained GAN into a
regression model, using as few as two labeled samples. This enables solving
regression tasks on datasets and attributes which are difficult to produce
quality supervision for. Additionally, we show that the same latent-distances
can be used to sort collections of images by the strength of given attributes,
even in the absence of explicit supervision. Extensive experimental evaluations
demonstrate that our method can be applied across a wide range of domains,
leverage multiple latent direction discovery frameworks, and achieve
state-of-the-art results in few-shot and low-supervision settings, even when
compared to methods designed to tackle a single task. |
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DOI: | 10.48550/arxiv.2107.11186 |