Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks
This addresses regularization challenges for deep learning practitioners, offering a method that reduces hyperparameter tuning and improves accuracy in image classification tasks.
The paper tackles overfitting in deep neural networks by introducing phantom embeddings to reduce inter-class similarity in latent space, achieving better generalization and outperforming standard baselines on CIFAR and FashionMNIST datasets.
The strength of machine learning models stems from their ability to learn complex function approximations from data; however, this strength also makes training deep neural networks challenging. Notably, the complex models tend to memorize the training data, which results in poor regularization performance on test data. The regularization techniques such as L1, L2, dropout, etc. are proposed to reduce the overfitting effect; however, they bring in additional hyperparameters tuning complexity. These methods also fall short when the inter-class similarity is high due to the underlying data distribution, leading to a less accurate model. In this paper, we present a novel approach to regularize the models by leveraging the information-rich latent embeddings and their high intra-class correlation. We create phantom embeddings from a subset of homogenous samples and use these phantom embeddings to decrease the inter-class similarity of instances in their latent embedding space. The resulting models generalize better as a combination of their embedding and regularize them without requiring an expensive hyperparameter search. We evaluate our method on two popular and challenging image classification datasets (CIFAR and FashionMNIST) and show how our approach outperforms the standard baselines while displaying better training behavior.