Regularization Techniques in Deep Learning
Regularization is a technique used in machine learning to prevent overfitting and improve the generalization performance of a model on unseen data. Overfitting occurs when a model learns to perform well on the training data but fails to generalize to new, unseen data. Regularization introduces a penalty term to the loss function, discouraging the model from fitting the training data too closely and promoting simpler or more regular patterns in the learned parameters. This helps prevent the model from capturing noise or irrelevant details in the training data, leading to better performance on new, unseen data.
In this blog, we will describe about some common regularization techniques:
- Dropout
- Drop Connect
- Batch Normalization
- Data Augmentation
- Fractional Max Pooling
- Stochastic Depth
1.Dropout: In Dropout, a random subset of neurons is temporarily excluded or “dropped out” during each iteration. This helps prevent overfitting by promoting more robust learning and reducing the reliance on specific neurons.
Dropout mimics ensemble learning during training by randomly deactivating a subset of neurons in each iteration, creating diverse network instances. Each instance can be viewed as a different model.
2. Drop Connect- Drop Connect has a similar flavour to dropout. However, instead of randomly dropping individual units (neurons) during training, DropConnect zero out some of the values of the weight matrix. This means for each training iteration, a random subset of connections in the neural network is set to zero.
3. Batch Normalization: Batch Normalization involves normalizing the inputs of each layer in a mini-batch by subtracting the mean and dividing by the standard deviation. This normalization helps address issues like internal covariate shift, ensuring that the inputs to each layer are centered and have a consistent scale during training.
Additionally, Batch Normalization introduces learnable parameters (gamma and beta) that allow the model to learn the optimal scale and mean for each feature.
Challenges: Performance can be sensitive to the choice of batch size. Extremely small batch sizes may lead to inaccurate estimation of batch statistics. and it requires using population statistics (mean and variance) computed during training.
4.Data Augmentation: Batch augmentation involves randomly applying diverse augmentations, such as rotations, flips, crops, or color adjustments, to the input data. Then, we train on those images rather than the original image. Random mix/combinations can be performed like translation, shearing , stretching, distorting depending on your creativity.
5. Fractional Max Pooling: A type of pooling operation used in convolutional neural networks (CNNs). While traditional max pooling divides the input into non-overlapping regions and selects the maximum value from each region, fractional max pooling randomize the pooling region and hence, allows for overlapping pooling regions with flexible sizes.
6. Stochastic Depth: In this method, some layers are randomly dropped during training and only some subsets are trained.
Regularization techniques play a crucial role in improving the generalization performance of machine learning models. By mitigating overfitting, these techniques help models generalize well to unseen data, enhancing their robustness and performance.
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