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Catastrophic Kukanganwa
Catastrophic forgetting occurs when training on new tasks degrades performance on previously learned tasks.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
Continual-learning methods attempt to balance adaptation and retention.
Kudzika Kwakadzika
Catastrophic forgetting occurs when learning new tasks degrades earlier capabilities. EWC penalizes changes to parameters important to earlier tasks; GEM constrains gradients using episodic examples under a local first-order approximation; iCaRL stores exemplars and uses class-mean features. LoRA freezes base weights and learns low-rank task updates, which may reduce direct interference but does not guarantee retention. The original RAG paper distinguishes sequence-level and token-level document marginalization. These mechanisms address different issues and require evaluation on old and new tasks.
Kuitwa muNyika yeChokwadi
Elastic Weight Consolidation penalizes changes to parameters estimated to be important for earlier tasks.
Experience replay mixes stored earlier examples with new-task examples during training.
LoRA keeps task-specific low-rank updates separate from frozen base weights in some adaptation settings.
Retrieval-augmented generation can consult an external index rather than storing every fact only in model parameters.
Ramangwana Renjodzi Kukanganwa
Evaluate old and new task performance after each update and make the retention criterion explicit. Choose methods based on data availability, storage and privacy constraints. Research benchmarks do not establish that any single technique prevents forgetting in all deployment settings.
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What is Catastrophic Forgetting?
Catastrophic forgetting occurs when training on new tasks degrades performance on previously learned tasks. Continual-learning methods attempt to balance adaptation and retention.
Ndeapi mamiriro anotsanangura njodzi yekukanganwa muneural network?
Kukanganwa kwakashata ndiko kurasikirwa nekukasira kwehunyanzvi hwekare kana network ichidzidziswa basa idzva, nekuti uremu hwakagovaniswa hunowedzera kunyorwa.
Sei kukanganwa kwenjodzi kuchiitika mumaneural network?
Zivo inogara muzvikamu zvakagovaniswa, saka kudzidziswa padata idzva kunochinja iwo uremu humwe uye kudzima kudzidza kwekutanga.
Chii chinoitwa neElastic Weight Consolidation (EWC) kuderedza kukanganwa?
EWC inowedzera chirango chenguva dzose chinononoka kugadziridza kune paramita anoonekwa akakosha kumabasa ekare, anofungidzirwa kuburikidza neFisher ruzivo.
Chiitiko chinodzokorora (rehearsal) kurwisa kukanganwa sei?
Rehearsal musanganiswa mumienzaniso yapfuura (yakachengetwa kana kugadzirwa) uchidzidza mabasa matsva kuti kuita kwekare kusimbiswe.
What does LoRA do to base-model weights when adding a task-specific adapter?
LoRA freezes base weights and trains low-rank updates for a task. This can reduce direct parameter interference but does not guarantee retention across every continual-learning sequence.
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