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Found 4 papers, 1 papers with code
Toward a Realistic Benchmark for Out-of-Distribution Detection
Deep neural networks are increasingly used in a wide range of technologies and services, but remain highly susceptible to out-of-distribution (OOD) samples, that is, drawn from a different distribution than the original training set.
Latent Diffusion Models for Attribute-Preserving Image Anonymization
Generative techniques for image anonymization have great potential to generate datasets that protect the privacy of those depicted in the images, while achieving high data fidelity and utility.
Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification
To explore this task, we leverage the power of computer-generated imagery to create, in a semi-automatic fashion, high-quality synthetic images of the same bike before and after a damage occurs.
Bridging the gap between Natural and Medical Images through Deep Colorization
Deep learning has thrived by training on large-scale datasets.
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