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Found 6 papers, 2 papers with code
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks
Biased attributes, spuriously correlated with target labels in a dataset, can problematically lead to neural networks that learn improper shortcuts for classifications and limit their capabilities for out-of-distribution (OOD) generalization.
ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations
The T2I prior model alone adds a billion parameters compared to the Latent Diffusion Models, which increases the computational and high-quality data requirements.
WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models
The rapid advancement of generative models, facilitating the creation of hyper-realistic images from textual descriptions, has concurrently escalated critical societal concerns such as misinformation.
Attributing Image Generative Models using Latent Fingerprints
This paper investigates the use of latent semantic dimensions as fingerprints, from where we can analyze the effects of design variables, including the choice of fingerprinting dimensions, strength, and capacity, on the accuracy-quality tradeoff.
Decentralized Attribution of Generative Models
Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement.
Scaling Distributed Machine Learning with In-Network Aggregation
Training machine learning models in parallel is an increasingly important workload.
