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Data augmentation transforms or combines training examples to improve learning from available data.
Sikiliza mwongozo huuInaonyeshwa na AI · ~1 min
An augmentation must preserve the target label or explicitly adjust it to match the transformed example.
Augmentation expands the training distribution through transformations or combinations. Mixup creates convex combinations of examples and labels. CutMix replaces a patch and mixes labels in proportion to area. AutoAugment searches for policies using validation performance, while back-translation creates synthetic paraphrases for text tasks. The methods are task-specific: transformations that preserve labels for one task may invalidate labels for another.
Random crops or flips can create altered image views when the label remains valid.
Mixup linearly combines two inputs and their labels.
CutMix pastes an image region from one example into another and weights labels by area.
Back-translation generates paraphrased text by translating through an intermediate language.
Select augmentations based on task semantics and validate on data representative of deployment. Record transformation distributions and test for synthetic artifacts or label mismatch. The cited papers report specific datasets and experiments, not a guarantee that more augmentation always improves generalization.
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Data augmentation transforms or combines training examples to improve learning from available data. An augmentation must preserve the target label or explicitly adjust it to match the transformed example.
Uboreshaji huunda nakala tofauti, zilizorekebishwa za data iliyopo ili kupunguza utumiaji wa kupita kiasi na kusaidia muundo kushughulikia ingizo zisizoonekana.
Kugeuza, kupunguza, kuzungusha na kubadilisha rangi ni nyongeza za kawaida za picha ambazo hubadilisha pikseli wakati wa kuhifadhi lebo.
Tafsiri ya nyuma huendesha maandishi kupitia lugha nyingine na kurudi nyuma, ikitoa toleo lililowekwa upya ambalo huhifadhi maana.
Mabadiliko hayafai kubadilisha jibu sahihi - kugeuza '6' hadi '9', kwa mfano, kunaweza kuweka mfano vibaya.
Mchanganyiko huunda sampuli mpya kwa kuchanganya kwa mstari jozi za pembejeo na lebo zao, kuhimiza mipaka ya maamuzi rahisi.
Endelea kujifunza
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InayofuataMwongozo unaofuata
Uboreshaji wa Data ya Sauti
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