SOTAVerified

Novel Concepts

Measures the ability of models to uncover an underlying concept that unites several ostensibly disparate entities, which hopefully would not co-occur frequently. This provides a limited test of a model's ability to creatively construct the necessary abstraction to make sense of a situation that it cannot have memorized in training.

Source: BIG-bench

Papers

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Showing 71–80 of 158 papers

TitleStatusHype
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts—0
Generative Pre-Trained Transformer for Design Concept Generation: An Exploration—0
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models—0
Assessing the Variety of a Concept Space Using an Unbiased Estimate of Rao's Quadratic Index—0
A Closer Look at Rehearsal-Free Continual Learning—0
Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models—0
Hyperbolic Learning with Synthetic Captions for Open-World Detection—0
Decision Making Using Rough Set based Spanning Sets for a Decision System—0
Image Inpainting by Patch Propagation Using Patch Sparsity—0
Influence zones for continuous beam systems—0
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