HyperAI초신경

홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

한국어

HyperAI초신경

Continual Learning On Visual Domain Decathlon

평가 지표

decathlon discipline (Score)

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	decathlon discipline (Score)	Paper Title	Repository
Res. adapt. finetune all	2643	Learning multiple visual domains with residual adapters	-
Piggyback	2838	Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights	-
Res. adapt.	2118	Learning multiple visual domains with residual adapters	-
DAN	2851	Incremental Learning Through Deep Adaptation	-
Res. adapt. decay	2621	Learning multiple visual domains with residual adapters	-
NetTailor	3744	NetTailor: Tuning the Architecture, Not Just the Weights	-
Res. adapt. dom-pred	2503	Learning multiple visual domains with residual adapters	-
Series Res. adapt.	3159	Efficient parametrization of multi-domain deep neural networks	-
LwF	2515	Learning without Forgetting	-
BN adapt.	1363	Universal representations:The missing link between faces, text, planktons, and cat breeds	-
Res. adapt. (large)	3131	Learning multiple visual domains with residual adapters	-
Depthwise Sharing	3234	Depthwise Convolution is All You Need for Learning Multiple Visual Domains	-
Depthwise Soft Sharing	3507	Depthwise Convolution is All You Need for Learning Multiple Visual Domains	-
Parallel Res. adapt.	3412	Efficient parametrization of multi-domain deep neural networks	-

0 of 14 row(s) selected.

Continual Learning On Visual Domain Decathlon | SOTA | HyperAI초신경