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

Showing 141150 of 158 papers

TitleStatusHype
Simple and Lightweight Human Pose EstimationCode0
Knowledge Graph Transfer Network for Few-Shot RecognitionCode0
WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant AnalysisCode0
SketchEmbedNet: Learning Novel Concepts by Imitating DrawingsCode0
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classificationCode0
How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue CorpusCode0
Emergence of hierarchical reference systems in multi-agent communicationCode0
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training DataCode0
A Clustering-based Framework for Classifying Data StreamsCode0
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel ConceptsCode0
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