Self-supervised learning differs from pure supervised learning in that the labels used by the learning process are contained in the data itself.

Question

BlackTom AI · Accepted Answer

Self-supervised learning generates supervisory signals from the input data itself by creating pretext tasks that produce labels without human annotation—therefore the statement is True.

1) Relevant knowledge points and concepts
- Supervised learning: models are trained using input–label pairs provided externally (human annotations or measurement).
- Self-supervised learning: a subset of unsupervised/representation learning where labels (often called pseudo-labels or targets) are derived automatically from the data via pretext tasks.
- Common pretext tasks: masked token prediction (BERT), predicting image rotations, colorization (predict color from grayscale), inpainting (predict missing patch), and contrastive tasks (treat different augmented views as positive pairs).

2) Complete reasoning process step by step
Step 1: Identify what “labels” means in learning: anything the model is trained to predict (class, masked tokens, rotation angle, etc.).
Step 2: In supervised learning, labels are external ground truth (human-provided classes/annotations).
Step 3: In self-supervised learning, labels are constructed from the data itself—for example, remove a patch and label it with the patch content, or mask tokens and label them with the original tokens.
Step 4: Since the labels originate from transformations or withheld parts of the input, they are “contained in the data itself.”
Step 5: Conclude that the defining distinction in the question is correct.

3) Calculations/intermediate steps
- No numeric calculations are required. Logical steps above serve as the intermediate reasoning.

4) Why the correct answer is correct
- The hallmark of self-supervised methods is automatic label creation using intrinsic structure of the data; this reduces or eliminates the need for external annotations while still providing supervision signals. Examples (BERT, SimCLR, rotation prediction) concretely demonstrate labels coming from the data.

5) Why the other option (False) is incorrect
- Claiming “False” would imply labels are not contained in the data, which contradicts the core mechanism of self-supervised learning where labels are explicitly derived from input data without manual labeling.

6) Conclusion
- The correct answer is True, which matches the provided answer.

Self-supervised learning differs from pure supervised learning in that the labels used by the learning process are contained in the data itself.判断题

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