HyperAI
Startseite
Neuigkeiten
Neueste Forschungsarbeiten
Tutorials
Datensätze
Wiki
SOTA
LLM-Modelle
GPU-Rangliste
Veranstaltungen
Suche
Über
Deutsch
HyperAI
Toggle sidebar
Seite durchsuchen…
⌘
K
Startseite
SOTA
Continual Learning
Continual Learning On Visual Domain Decathlon
Continual Learning On Visual Domain Decathlon
Metriken
decathlon discipline (Score)
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
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.
Previous
Next