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Continual learning updates a model over a sequence of tasks or data streams while trying to retain useful prior capabilities.
Déglul tegtal biiIA-nettali · ~1 min
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Résumé
Catastrophic forgetting is a common failure where learning new tasks degrades earlier performance.
Plongeur bu xóot
EWC uses a Fisher-information-based regularizer to discourage changes to parameters considered important for prior tasks. GEM uses episodic examples to constrain gradient updates; its guarantee concerns a local linear approximation to previous-task losses, so actual finite updates can still change performance. iCaRL stores exemplars, selects representatives with a herding procedure and classifies using per-class mean features. Replay methods revisit retained examples; these methods have different memory, privacy and compute tradeoffs. The stability-plasticity tradeoff describes balancing retention with learning new tasks.
Doxal ci àdduna dëgg
Evaluate old and new tasks after each update.
Use replay samples when permitted by data policy.
Compare regularization methods such as EWC against fine-tuning.
Record task order and memory budget.
Ëlëgu jàng bu wéy ak fàtte bu metti
Continual-learning results depend on task sequence, model, data access, and evaluation protocol. Report both new-task learning and old-task retention.
Teg ko ci jëf
Lu tax mu am solo
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- Njëgg ak budget. Mën nga laaj laaj yu gëna baax ci samp gi balaa ngay dugal xaalis wala sa jotu liggéey.
- Ekip ak def liggéey. Ekip yi bokk xam-xam ñoo gëna mëna jël yenn dogal ci wàllu produit, politik ak jàng.
Moytul
- Ekip yu bari mën nañu jëfandikoo benn baat ci anam wu wuute, kon teela leeral yaatuwaayam.
- Benchmark yi mën nañu nuru lu am doole waaye performance yi ci àdduna bi duñu tolloo.
- Bëgg kalite done ak palaŋu jàngat dafay faral di jur njariñ yu yomba dagg.
Jéego yi ci topp
- Tàmbaleel ci joxe leeral ci làkk wu leer ci njariñ li nga soxla.
- Tannal benn metric bu baax ak benn anam bu baaxul balaa ngay saytu.
- Doxal ab pilote bu ndaw ak ay done yu representatif, du ab demo bu leer.
- Dokumenteer fu jàng bu wéy ak fàtte Catastrophic di jàppale ak fu pexe yu gëna yomba gëna baax.
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Nattal sa bopp
Take the Continual Learning and Catastrophic Forgetting quiz
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Laaj yi ñuy faral di laaj
What is Continual Learning and Catastrophic Forgetting?
Continual learning updates a model over a sequence of tasks or data streams while trying to retain useful prior capabilities. Catastrophic forgetting is a common failure where learning new tasks degrades earlier performance.
Luy 'fatte bu metti'?
Fatte bu metti mooy ñàkka mëna def liggéey bu yàgg sudee dañuy soppi poids yiñ bokk ci reso bi, fekk ñu ngi jàng lu bees.
Luy boole poid elastik (EWC)?
EWC yokk na penalti (jëfandikoo xibaaru Fisher) buy ancre poid yi ñu jàpp ni dañu am solo ci liggéey yu yàgg yi, wàññi fàtte.
naka la pexe yu lalu ci replay di xeexe fàtte?
Replay dafay jaxase misaal yu njëkk yi ci ay lote yu bees suko defee gradient yi wane liggéey yu yàgg yi ak yu bees yi, di jegesi done yiñ boole.
How does Gradient Episodic Memory (GEM) constrain a gradient update while learning a new task?
GEM projects updates using episodic-memory losses so the local first-order approximation does not increase loss on previous-task examples. This constraint is based on a local linearization and does not guarantee actual loss will never rise after every finite step.
Lan moo waral ñu fàtte liggéey bu bees bi nga def ci liggéey bu bees?
Coppite gradient yi amul ay tënk dañuy binndaat poids yiñ bokk ci optimum bu bees bi, bàyyi gox yi baaxoon ci liggéey yu njëkk ya.
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