- Capsule Network
- Capsule Neural Networks
- Causal Inference
- Character Recognition
- Classification
- Clustering Analysis
- Co-Active Learning
- Co-Training
- Cognitive Architecture
- Cognitive Computing
- Collaborative Filtering
- Combinatorial Optimization
- Common Sense Reasoning
- Compositional Pattern-Producing Networks (CPPNs)
- Computational Creativity
- Computer Vision
- Concept Drift
- Concept Learning
- Constrained Optimization
- Content-Based Recommender Systems
- Contextual Bandits
- Contrastive Divergence
- Contrastive Learning
- Conversational Agents
- Convolutional Autoencoder
- Convolutional Encoder-Decoder Network
- Convolutional Long Short-Term Memory
- Convolutional Long Short-Term Memory (ConvLSTM)
- Convolutional Neural Gas
- Convolutional Neural Network
- Convolutional Recurrent Neural Network
- Convolutional Sparse Autoencoder
- Convolutional Sparse Coding
- Cross entropy loss
- Crossover
- Curriculum Learning
- Cyber Physical System
- Cyclical Learning Rate

# What is Contrastive Divergence

**Introduction:**

Contrastive Divergence is a popular algorithm in the field of unsupervised learning, a learning method in which the model learns from data without being explicitly told what to learn. The Contrastive Divergence algorithm was proposed by Geoffrey Hinton and his research team in 2002. Their ground-breaking paper described a method for training an energy-based model using gradient descent.
This article will cover the following topics:
- What is Contrastive Divergence?
- How does Contrastive Divergence work?
- Applications of Contrastive Divergence
- Limitations of Contrastive Divergence
- Conclusion

**What is Contrastive Divergence?**

Contrastive Divergence is a type of Markov chain Monte Carlo (MCMC) algorithm used for parameter estimation in probabilistic models. These models aim to estimate the probability distribution of a set of observed data given a set of parameters. Contrastive Divergence learns the parameters by sampling from the probability distribution and iteratively adjusting the parameters to fit the observed data. Contrastive Divergence is often used in Deep Learning and Neural Networks as a tool for unsupervised feature learning. Feature learning is the process of identifying and extracting features from raw data that can be used for a particular task. Unsupervised feature learning refers to the case where the features are learned automatically from data without the use of labels or supervision.

**How does Contrastive Divergence work?**

- Initialize the model parameters
- Given an input data point, sample from the model to obtain a positive phase sample
- Starting with the positive phase sample, Gibbs sample k times to obtain a negative phase sample
- Update the model parameters using the difference between the positive and negative phase samples
- Repeat steps 2-4 until convergence is reached

**Applications of Contrastive Divergence**

**Limitations of Contrastive Divergence**

**1. Inability to optimize the partition function:**The partition function is a normalization constant that ensures the probabilities sum to one. It is notoriously difficult to compute for energy-based models, and Contrastive Divergence does not optimize it. This can lead to problems in models with many hidden units.

**2. Lack of solid theoretical grounding:**The theory behind Contrastive Divergence is still not well understood. While it has been shown to work well in practice, there is no clear theoretical justification for why it works.

**3. Sensitivity to the number of Gibbs steps:**The number of Gibbs steps used in Contrastive Divergence can significantly affect the performance of the algorithm. Too few Gibbs steps can lead to bias in the estimates, while too many can lead to high variance.

**Conclusion**