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 3140 of 158 papers

TitleStatusHype
DER: Dynamically Expandable Representation for Class Incremental LearningCode1
Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster MemoryCode1
DreamCreature: Crafting Photorealistic Virtual Creatures from ImaginationCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object InteractionsCode1
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot LearningCode1
SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained ModelsCode1
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure AbductionCode0
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of ImagesCode0
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature SpaceCode0
Show:102550
← PrevPage 4 of 16Next →

No leaderboard results yet.