Psuedo-Labels

In Self-supervised learning, the core idea is to leverage the structure within the data itself to create tasks that generate **pseudo-labels . These tasks allow the model to learn meaningful representations from the data without requiring manual labelling.

What are Pseudo-Labels?

Pseudo-labels are automatically generated labels derived from the data itself. These labels are not manually annotated but are inferred based on the inherent structure or attributes of the data.

How are Pseudo-Labels Created?

1. Masking/Prediction Tasks:

• Example: In NLP, when training a model like BERT, random words in a sentence are masked. The model is trained to predict these masked words using the context (masked language modeling).
  • Input: “The cat is ___ the table.”
  • Pseudo-label: “on.”

2. Contrastive Tasks:

• Example: In computer vision, contrastive learning involves generating two augmented views of the same image and training the model to recognize that they come from the same source.
  • Input: Original image and its augmented version.
  • Pseudo-labels: “Same” for these pairs, “Different” for unrelated images.

3. Spatial Prediction:

• Example: For images, patches from different regions can be swapped, and the task is to predict their correct positions.
  • Input: Image patches in shuffled order.
  • Pseudo-labels: Their actual positions.

4. Temporal Prediction:

• Example: In video data, frames are shuffled, and the model is trained to predict their correct sequence.
  • Input: Frames [3, 1, 2].
  • Pseudo-labels: [1, 2, 3] (actual order).

5. Clustering-based Labeling:

• Example: For datasets like ImageNet, clustering algorithms (e.g., K-means) group similar samples. The model is then trained on these cluster assignments as pseudo-labels.
  • Input: Images.
  • Pseudo-labels: Cluster IDs.

Why Use Pseudo-Labels?

• No Manual Annotation Required: Removes the need for expensive and time-consuming manual labelling.
• Rich Representations: By solving these self-defined tasks, the model learns general and transferable features that are useful for downstream supervised tasks.
• Scalability: Enables training on massive datasets without the bottleneck of acquiring labels.

Examples in Practice:

1. NLP:
   • BERT: Predicts masked tokens in a sentence and determines if two sentences are contiguous.
   • GPT: Predicts the next word in a sequence (causal language modeling).
2. Vision:
   • SimCLR: Maximizes similarity between different augmented views of the same image and minimizes similarity with others.
   • MAE (Masked Autoencoders): Predicts the missing regions in an image.
3. Speech:
   • Wav2Vec: Predicts quantized representations of masked audio segments.

How Does It Help?

These tasks train models to understand the underlying patterns and structures in data, enabling the learned representations to be applied to a variety of downstream tasks, such as classification, regression, or clustering, often requiring fewer labeled examples.