Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Idanwo Ẹkọ Diẹ-Shot

Compare in-context demonstrations and metric-based few-shot learning, with support/query roles, prototype calculations, notation, and prompt ordering.

Jẹmọ itọsọna awọn ọnaDiẹ Shot Learning
Ibeere 1 ti 8

In an N-way K-shot classification episode, what does K count?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. In an N-way K-shot classification episode, what does K count?
    • The number of classes
    • Labeled support examples per class
    • All parameters in the model
    • The number of optimization epochs
  2. Nigbati awoṣe ede ti o tobi ba ṣe ni inu-itumọ-ọrọ ẹkọ diẹ-shot lati itọsi kan, kini o ṣẹlẹ si awọn iwuwo rẹ?
    • Wọn ti wa ni itanran-aifwy die-die lori awọn apẹẹrẹ kiakia
    • Wọn ti tunto si aaye ayẹwo iṣẹ-ṣiṣe kan
    • Wọn duro ko yipada; Awọn apẹẹrẹ nikan ni ipo asọtẹlẹ naa
    • Wọn jẹ aropin pẹlu awọn ifibọ apẹẹrẹ
  3. Kini iyatọ bọtini laarin odo-shot ati itọsi-shot diẹ?
    • Zero-shot pese ko si sise apeere; diẹ-shot pese kan diẹ
    • Zero-shot nlo awoṣe ti o kere ju diẹ-shot
    • Zero-shot nigbagbogbo n ṣiṣẹ daradara ju diẹ-shot
    • Zero-shot nilo ike data ikẹkọ nigba ti diẹ-shot ko
  4. How can prior learning contribute to a few-shot task?
    • It guarantees every arbitrary demonstration yields the intended rule
    • It requires copying each example as every final answer
    • It necessarily retrains all network weights during prompting
    • It can supply useful learned patterns or representations for interpreting the small support set
  5. Ewo ni o ṣapejuwe ọna ti o da lori iwọn-metiriki bi nẹtiwọọki afọwọṣe kan?
    • O kọ awọn aami apẹẹrẹ sinu ibi ipamọ data fun wiwa
    • O ṣe agbekalẹ awọn apẹẹrẹ sintetiki titi ti o fi ni ẹgbẹẹgbẹrun
    • O ṣe afiwe apẹẹrẹ tuntun si ifisinu apapọ ti awọn apẹẹrẹ diẹ ti kilasi kọọkan ati yan eyi ti o sunmọ julọ
    • O nlo ẹkọ imuduro lati awọn esi eniyan
  6. Oniseṣẹ kan rii pe ṣiṣatunto awọn apẹẹrẹ nirọrun ni iyara-isun diẹ ṣe iyipada deede. Kí ni èyí ṣàkàwé?
    • Kokoro hardware kan ninu GPU
    • Iṣe-shot diẹ yẹn le jẹ ifarabalẹ si aṣẹ apẹẹrẹ ati ọna kika
    • Wipe awọn awoṣe ti wa ni overfitting si awọn igbeyewo ṣeto
    • Odo-shot yoo jẹ soro
  7. A class has embedded support points (0,0) and (2,0). What arithmetic-mean prototype does the stated prototypical method form?
    • (2,0)
    • (0,2)
    • (2,2)
    • (1,0)
  8. Which distinction defines support and query roles in the original prototypical-network episode?
    • Support labels construct class prototypes; separate query items evaluate classification
    • Query labels must be copied into every support prototype
    • Support items contain no labels by definition
    • The two sets must always contain exactly the same sampled examples