Awọn ipilẹ Itọsọna
Ẹkọ Ilọsiwaju ati Igbagbe Ajalu
Continual learning updates a model over a sequence of tasks or data streams while trying to retain useful prior capabilities.
Gbọ si itọsọna yiiAI-ṣe afihan · ~1 min
Lori iwe yi1 iseju kika
Akopọ
Catastrophic forgetting is a common failure where learning new tasks degrades earlier performance.
Jin Dive
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.
Real-World imuse
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.
Ọjọ iwaju ti Ẹkọ Ilọsiwaju ati Igbagbe Ajalu
Continual-learning results depend on task sequence, model, data access, and evaluation protocol. Report both new-task learning and old-task retention.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Awọn ipinnu diẹ sii. O ṣe iranlọwọ fun ọ lati ya sọtọ awọn iṣeduro imọ-ẹrọ lati ede tita.
- Iye owo ati isuna. O le beere awọn ibeere imuse to dara julọ ṣaaju lilo owo tabi akoko.
- Ẹgbẹ ati ṣiṣan iṣẹ. Awọn ẹgbẹ pẹlu oye pinpin ṣe ọja to dara julọ, eto imulo, ati awọn ipinnu ikẹkọ.
Ṣọra fun
- Awọn ẹgbẹ oriṣiriṣi le lo ọrọ kanna ni oriṣiriṣi, nitorinaa ṣalaye iwọn ni kutukutu.
- Awọn aṣepari le wo lagbara lakoko ti iṣẹ-aye gidi ko ṣe deede.
- Aibikita didara data ati awọn ero igbelewọn nigbagbogbo ṣẹda awọn abajade ẹlẹgẹ.
Next awọn igbesẹ
- Bẹrẹ pẹlu itumọ-ede itele ti abajade ti o nilo.
- Mu metiriki aṣeyọri kan ati ipo ikuna kan ṣaaju idanwo.
- Ṣiṣe awakọ kekere kan pẹlu data aṣoju, kii ṣe eto demo didan.
- Iwe-ipamọ nibiti Ẹkọ Ilọsiwaju ati Igbagbe Ajalu ṣe iranlọwọ ati nibiti awọn ọna ti o rọrun dara julọ.
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the Continual Learning and Catastrophic Forgetting quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
Kini Ẹkọ Lemọlemọfún ati Igbagbe Ajalu?
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.
Kini 'igbagbe ajalu'?
Igbagbe ajalu jẹ ipadanu didasilẹ ti iṣẹ ṣiṣe atijọ nigbati awọn iwuwo pinpin nẹtiwọọki kan ti kọkọ kọ lakoko ti o nkọ nkan tuntun.
Kini Isọdọtun Iwọn Elastic (EWC) ṣe?
EWC ṣafikun ijiya kan (lilo alaye Fisher) ti o dakọ awọn iwuwo ti a ro pe o ṣe pataki fun awọn iṣẹ ṣiṣe atijọ, idinku igbagbe.
Bawo ni awọn ọna ti o da lori atunṣe ṣe koju igbagbe?
Sisisẹsẹhin dapọ awọn apẹẹrẹ ti o kọja sinu awọn ipele tuntun ki awọn gradients ṣe afihan atijọ ati awọn iṣẹ ṣiṣe tuntun, isunmọ data apapọ atilẹba.
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.
Kini idi ti iṣatunṣe itanran ti o rọrun lori iṣẹ-ṣiṣe tuntun n fa igbagbe?
Awọn imudojuiwọn gradient ti ko ni ihamọ tunkọ awọn iwuwo pinpin si ọna aipe tuntun, fifisilẹ awọn agbegbe ti o dara fun awọn iṣẹ ṣiṣe iṣaaju.
Tesiwaju kikọ
Jẹmọ awọn itọsọna
Awọn itọsọna diẹ sii ti a yan fun koko yii