Maswali Machache ya Kujifunza

Majibu na maelezo huonekana unapojibu maswali.

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. Wakati modeli kubwa ya lugha inapojifunza katika muktadha wa risasi chache kutoka kwa haraka, nini hufanyika kwa uzani wake?
   • Zimepangwa vizuri kidogo kwenye mifano ya haraka
   • Zimewekwa upya kwa kituo cha ukaguzi maalum cha kazi
   • Wanakaa bila kubadilika; mifano inaweka tu utabiri
   • Wao ni wastani na upachikaji wa mfano
3. Ni tofauti gani kuu kati ya msukumo wa risasi-sifuri na ushawishi wa risasi chache?
   • Zero-risasi haitoi mifano iliyofanya kazi; chache-risasi hutoa chache
   • Sufuri-risasi hutumia modeli ndogo kuliko picha chache
   • Picha sifuri daima hufanya kazi vizuri zaidi kuliko risasi chache
   • Upigaji picha sifuri unahitaji data ya mafunzo yenye lebo ilhali shoti chache hazihitaji
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. Ni ipi inaelezea mbinu ya picha chache kulingana na metri kama mtandao wa mfano?
   • Inaandika lebo za mfano kwenye hifadhidata ya utaftaji
   • Inazalisha mifano ya syntetisk hadi ina maelfu
   • Inalinganisha sampuli mpya na wastani wa upachikaji wa mifano michache ya kila darasa na kuchagua iliyo karibu zaidi
   • Inatumia mafunzo ya kuimarisha kutoka kwa maoni ya kibinadamu
6. Mtaalamu hupata kwamba kupanga upya mifano kwa haraka-haraka hubadilisha usahihi. Je, hili linaonyesha nini?
   • Hitilafu ya maunzi kwenye GPU
   • Utendaji huo wa picha chache unaweza kuwa nyeti kwa mpangilio wa mfano na umbizo
   • Kwamba mfano unafaa zaidi kwa seti ya majaribio
   • Hiyo sifuri-risasi isingewezekana
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