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Found 6 papers, 4 papers with code
A Geometric Framework for Understanding Memorization in Generative Models
To better understand this phenomenon, we propose the manifold memorization hypothesis (MMH), a geometric framework which leverages the manifold hypothesis into a clear language in which to reason about memorization.
MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation
Text-to-SQL generation enables non-experts to interact with databases via natural language.
Ranked #5 on
Text-To-SQL
on spider
Data-Efficient Multimodal Fusion on a Single GPU
The goal of multimodal alignment is to learn a single latent space that is shared between multimodal inputs.
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Comparing to 17 modern metrics for evaluating the overall performance, fidelity, diversity, rarity, and memorization of generative models, we find that the state-of-the-art perceptual realism of diffusion models as judged by humans is not reflected in commonly reported metrics such as FID.
Ranked #3 on
Image Generation
on FFHQ 256 x 256
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation
We propose TR0N, a highly general framework to turn pre-trained unconditional generative models, such as GANs and VAEs, into conditional models.
Ranked #30 on
Text-to-Image Generation
on MS COCO
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
