Contrastive Learning • SimCLR • MoCo • Masked Autoencoders
⭐ 1. What Is Self-Supervised Learning?
Self-supervised learning teaches a model by using the data itself to generate training tasks.
The model learns meaningful features without human labels.
Simple Explanation
Imagine learning a puzzle by first breaking a picture and then trying to rebuild it.
You don't need a teacher; you learn from the picture itself.
That is self-supervised learning.
⭐ 2. Contrastive Learning
Contrastive learning teaches a model to understand what things are similar and what things are different.
How It Works
- Take an image
- Create two different versions (called augmentations)
- One may be brighter
- One may be flipped
- The model must learn that these two images are still the same object
- Another image is used as a negative example
- Model learns:
- Positive pair → should be similar
- Negative pair → should be different
Real Example
Take a picture of your face:
- Bright version → still you
- Rotated version → still you
- Another person → negative example
The model learns identity without labels.
⭐ 3. SimCLR
SimCLR is a famous contrastive learning method developed by Google.
Key Ideas
- Take one image
- Create two random augmented versions
- Pass both through the same neural network
- Use a contrastive loss to pull similar images together
- Push dissimilar images apart
Why SimCLR Works Well
- Uses strong data augmentation
- Very simple architecture
- Learns extremely good image features
Code Example (Simplified TensorFlow)
import tensorflow as tf
def augment(image):
image = tf.image.random_flip_left_right(image)
image = tf.image.random_brightness(image, 0.3)
return image
# Two augmented views of the same image
img1 = augment(image)
img2 = augment(image)
# Encode using shared network
z1 = encoder(img1)
z2 = encoder(img2)
# Contrastive training step (Simplified)
loss = contrastive_loss(z1, z2)⭐ 4. MoCo (Momentum Contrast)
MoCo, developed by Facebook AI, is another powerful contrastive learning strategy.
Why MoCo Is Special
MoCo maintains a large queue of negative samples, which makes training stable and efficient.
Simple Explanation
MoCo keeps a memory bank of features from:
- Past batches
- Different augmentations
This gives many diverse negative examples, improving learning.
Benefits
- Works even with small batch sizes
- Learns strong visual features
- Great for low-resource environments
⭐ 5. Masked Autoencoders (MAE)
Masked Autoencoders are self-supervised models where part of the input is hidden (masked), and the model must guess the missing part.
How It Works (Images)
- Take an image
- Mask (hide) 50–75% of patches
- Encoder sees only the visible patches
- Decoder reconstructs the full image
Why It Works
The model learns:
- Shapes
- Patterns
- Object structures
- High-level understanding
Real Example
Like solving a jigsaw puzzle where you only see half the pieces.
Your brain still figures out the full picture.
Simplified Code (PyTorch Style)
import torch
import torch.nn as nn
class MaskedAutoencoder(nn.Module):
def __init__(self):
super().__init__()
self.encoder = nn.Linear(128, 64)
self.decoder = nn.Linear(64, 128)
def forward(self, x, mask):
x_masked = x * mask # hide some patches
z = self.encoder(x_masked)
out = self.decoder(z)
return out6. Why Self-Supervised Learning Is Important
| Benefit | Explanation |
|---|---|
| Uses unlabeled data | No need for expensive labels |
| Learns strong features | Useful for many tasks |
| Works for images, audio, text | Very flexible |
| Improves small datasets | Helps when labeled data is limited |
Today's most advanced models—including GPT, BERT, and Vision Transformers—use self-supervised learning.
7. Real-World Applications
A. Computer Vision
- Medical imaging
- Satellite image understanding
- Face recognition
- Robotics navigation
B. Natural Language Processing
- Sentence completion
- Masked language models (like BERT)
- Text prediction (GPT uses next-token prediction)
C. Audio & Speech
- Speech recognition
- Voice filters
- Noise reduction
D. Video Understanding
- Action recognition
- Surveillance analysis
⭐ 8. Summary Table
| Method | Key Idea | Example Use |
|---|---|---|
| Contrastive Learning | Similar vs. different | Face identity learning |
| SimCLR | Augmented views | Image classification |
| MoCo | Memory queue | Low-batch training |
| Masked Autoencoders | Predict missing parts | Vision Transformers |