Self-Supervised Learning

Beginner Explanation

Imagine you’re trying to solve a jigsaw puzzle, but you don’t have the picture on the box to guide you. Instead, you figure out how the pieces fit together by looking at the colors and shapes. Self-supervised learning works in a similar way. It takes a big pile of information (like pictures or text) and learns patterns without needing labels. For example, it might learn to guess what the missing piece of a picture is based on the pieces around it. This helps the model understand the data better, just like you get better at puzzles the more you practice.

Technical Explanation

Self-supervised learning (SSL) is a paradigm where models learn from unlabeled data by creating supervisory signals from the data itself. For instance, in image processing, a model might be trained to predict the rotation angle of an image. The model is trained on a dataset of images where some images are randomly rotated. The objective is to minimize the loss between the predicted rotation and the actual rotation. In Python, using PyTorch, this can be implemented as follows: “`python import torch import torch.nn as nn import torchvision.transforms as transforms from torchvision import datasets class SimpleModel(nn.Module): def __init__(self): super(SimpleModel, self).__init__() self.fc = nn.Linear(256, 4) # Assuming input features are 256, predicting 4 angles def forward(self, x): return self.fc(x) # Data loading and transformations transform = transforms.Compose([transforms.Resize((128, 128)), transforms.ToTensor()]) train_dataset = datasets.ImageFolder(root=’path/to/data’, transform=transform) train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=32, shuffle=True) # Training loop (simplified) model = SimpleModel() criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters()) for images, labels in train_loader: optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() “`

Academic Context

Self-supervised learning is a rapidly growing area of research that leverages unlabeled data to learn useful representations. It is based on the premise that many tasks can be solved by generating labels from the data itself. Key techniques include contrastive learning, where the model learns to differentiate between similar and dissimilar data points, and predictive coding, where parts of the input are predicted from other parts. Notable papers include ‘A Simple Framework for Contrastive Learning of Visual Representations’ (SimCLR) by Chen et al. (2020) and ‘BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding’ by Devlin et al. (2018), which demonstrate the effectiveness of SSL in both vision and language domains.

Code Examples

Example 1:

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torchvision import datasets

class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(256, 4)  # Assuming input features are 256, predicting 4 angles

    def forward(self, x):
        return self.fc(x)

# Data loading and transformations
transform = transforms.Compose([transforms.Resize((128, 128)), transforms.ToTensor()])
train_dataset = datasets.ImageFolder(root='path/to/data', transform=transform)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=32, shuffle=True)

# Training loop (simplified)
model = SimpleModel()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())

for images, labels in train_loader:
    optimizer.zero_grad()
    outputs = model(images)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

Example 2:

def __init__(self):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(256, 4)  # Assuming input features are 256, predicting 4 angles

Example 3:

def forward(self, x):
        return self.fc(x)

Example 4:

optimizer.zero_grad()
    outputs = model(images)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

Example 5:

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torchvision import datasets

Example 6:

import torch.nn as nn
import torchvision.transforms as transforms
from torchvision import datasets

class SimpleModel(nn.Module):

Example 7:

import torchvision.transforms as transforms
from torchvision import datasets

class SimpleModel(nn.Module):
    def __init__(self):

Example 8:

from torchvision import datasets

class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()

Example 9:

class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(256, 4)  # Assuming input features are 256, predicting 4 angles

Example 10:

    def __init__(self):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(256, 4)  # Assuming input features are 256, predicting 4 angles

    def forward(self, x):

Example 11:

    def forward(self, x):
        return self.fc(x)

# Data loading and transformations
transform = transforms.Compose([transforms.Resize((128, 128)), transforms.ToTensor()])

View Source: https://arxiv.org/abs/2511.16674v1

Beginner Explanation

Technical Explanation

Academic Context

Code Examples

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