Idanwo-Aago Augmentation adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Ni akoko itọkasi, kini afikun akoko idanwo ṣe pẹlu titẹ sii kan?
   • Ṣiṣe awoṣe ti o wa titi lori ọpọlọpọ awọn ẹda ti o yipada ti titẹ sii ati apapọ awọn abajade
   • Atunṣe awoṣe lori data ti a ti pọ si ni gbogbo igba ti o ba beere
   • Fifi ariwo si awọn iwọn awoṣe ṣaaju asọtẹlẹ kọọkan
   • Compressing awoṣe ki o nṣiṣẹ yiyara ni itọkasi
2. When can pooling predictions from transformed views reduce prediction variance?
   • When every view changes the true class
   • When the transforms preserve the label and the view errors are at least partly uncorrelated
   • When all predictions are identical by design
   • When the model is retrained on each transformed copy
3. Kini idiyele akọkọ ti lilo TTA?
   • O nilo data idanwo aami
   • O paarọ awọn iwuwo ikẹkọ patapata
   • O mu iṣiro inference pọ si nitori awoṣe nṣiṣẹ ni igba pupọ fun apẹẹrẹ
   • O dinku deede awoṣe lori awọn igbewọle mimọ
4. Aṣayan imudara wo ni o le ṣe ipalara classifier oni-nọmba kan gangan?
   • Imọlẹ kekere yipada
   • Awọn irugbin laileto kekere
   • Nfi ariwo Gaussian kekere kan kun
   • Ni petele yiyi '6' kan ki o le dabi '9'
5. Ni ipin, igbesẹ afikun wo ni o nilo ṣaaju aropin awọn abajade TTA?
   • Yipada iyipada jiometirika kọọkan ki awọn iboju iparada ti a sọtẹlẹ ṣe deede pẹlu awọn piksẹli atilẹba
   • Tunṣe oluyipada fun wiwo kọọkan
   • Sisọ gbogbo awọn iwo ti o yipada kuro
   • Yiyipada awọn iboju iparada si awọn apoti didi
6. For a classification model, what may be aggregated across test-time views?
   • The model’s training labels
   • The predicted probabilities or logits from the views
   • The input image file names
   • The optimizer momentum buffers
7. Does standard test-time augmentation require retraining the model on new labels?
   • Yes; each view needs a newly trained model
   • Yes; it requires extra class labels for every view
   • No; it reuses fixed weights and evaluates transformed inputs at inference
   • No; it replaces the model with a lookup table
8. A segmentation model predicts masks on flipped and cropped views. What must happen before pixelwise pooling?
   • Average masks before undoing the transforms
   • Discard the original image coordinates
   • Apply a different label to each pixel view
   • Invert the geometric transforms so all masks align with the original image coordinates