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自监督学习测验

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问题 1 的 8

Where does the supervision signal in the described self-supervised examples come from?

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  1. Where does the supervision signal in the described self-supervised examples come from?
    • A requirement for no training objective
    • Only new human semantic labels for every pretraining example
    • A rule that examples contain no useful information
    • The data and defined transformations of it
  2. Which input positions contribute prediction targets to the original BERT masked-language-model loss?
    • The positions selected for the MLM prediction task
    • Every position in the entire input regardless of selection
    • Only the first position of each new sentence
    • Only positions that appear after the final sentence boundary
  3. 在 SimCLR 等对比学习中，“正对”是：
    • 两张完全不相关的图片
    • 一对人工书写的标签
    • 同一图像的两个增强视图在嵌入空间中应该接近
    • 数据集的两个副本
  4. What annotation requirement can self-supervised pretraining reduce in the described examples?
    • The need for any usable input data
    • Separate task-specific semantic labels for every pretraining example
    • All computational cost by definition
    • The need to assess downstream performance
  5. How can a representation learned through self-supervision be used for a later task?
    • By evaluating a task head or adapting the representation through fine-tuning
    • By assuming every target task requires no labels or validation
    • By discarding all representations before making predictions
    • By treating the pretraining loss as proof of every application's accuracy
  6. GPT 式模型的自监督任务与 BERT 有何不同？
    • 它根本不使用任何文字
    • 每个单词都需要手动标签
    • 它仅预测文档的第一个单词
    • 它预测下一个标记，而不是填充屏蔽标记
  7. In the original SimCLR framework, which component is discarded for the reported downstream representation use?
    • The encoder representation itself
    • The contrastive projection head
    • All training inputs before pretraining begins
    • The positive-pair source-image relationship
  8. When original BERT pretraining replaces a selected input position with a random token, what remains its loss target?
    • The newly inserted random token
    • The next sentence's first token in every case
    • The original token before replacement
    • No target, because every random replacement is omitted from training