가이드 연계 퀴즈 · 중간 레벨

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자체 감독 손실, 내부 모델 상태, 선형 스케일링, 외부 루프 학습을 포함한 테스트 시간 적응 및 TTT 시퀀스 레이어를 비교합니다.

관련 가이드 경로시험 시간 훈련
질문 1 의 8

What happens in the cited classic test-time training adaptation procedure before the main prediction?

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  1. What happens in the cited classic test-time training adaptation procedure before the main prediction?
    • The unknown main-task label is supplied as a correct training target
    • The model must delete all parameters
    • Every earlier training example is reloaded without adaptation
    • Model parameters are updated using the incoming unlabeled sample
  2. 클래식 TTT가 자체 감독 보조 작업에 의존하는 이유는 무엇입니까?
    • 자기주도는 항상 본업보다 정확하기 때문에
    • 테스트 시에는 레이블을 사용할 수 없으므로 레이블 없이도 계산할 수 있는 손실이 필요합니다.
    • 신경망이 필요하지 않기 때문입니다.
    • 테스트 데이터를 암호화하려면
  3. What forms the recurrent hidden state of a TTT sequence layer?
    • The parameters of an inner model updated through learning
    • Only an unchanging raw input filename
    • A mandatory list of all previous full attention matrices
    • The vocabulary sorted alphabetically
  4. Under fixed inner-model dimensions and update work, how does TTT sequence-layer complexity scale with sequence length?
    • Quadratically for every possible implementation
    • Independently of processing any tokens
    • Linearly in sequence length
    • Exponentially because every token retrains from all possible sequences
  5. What problem motivated the cited classic test-time adaptation study?
    • A requirement to increase the alphabet size
    • A difference between training and test input distributions
    • A guarantee that all deployment inputs match training
    • A need to sort source filenames faster
  6. Why is a decrease in an auxiliary adaptation loss insufficient by itself to establish a successful main-task update?
    • Every auxiliary loss is the unknown main-label accuracy
    • A loss value proves perfect safety on every future sample
    • The auxiliary objective and the intended prediction measure are different
    • An auxiliary task cannot be computed on unlabeled input
  7. What inner models distinguish the studied TTT-Linear and TTT-MLP layer variants?
    • A decision tree and a mandatory image tokenizer
    • Two copies of the same fixed dictionary with no parameters
    • A full historical attention matrix and an alphabetical index
    • A linear model and a two-layer multilayer perceptron
  8. In the sequence-layer framework, which loop learns the parameters defining the inner self-supervised task?
    • The outer training loop
    • Only the unknown future test label
    • A required manual dictionary edit at every token
    • Neither loop can learn any task parameters