Partial Graph Reasoning for Neural Network Regularization
This work addresses regularization for neural networks, offering an incremental improvement over Dropout by reducing feature correlations without modifying the base model during inference.
The authors tackled the problem of feature co-adaptation in deep neural networks by introducing DropGraph, a regularization method that uses partial graph reasoning to distort feature maps, outperforming state-of-the-art regularizers across 4 tasks and 8 datasets.
Regularizers help deep neural networks prevent feature co-adaptations. Dropout, as a commonly used regularization technique, stochastically disables neuron activations during network optimization. However, such complete feature disposal can affect the feature representation and network understanding. Toward better descriptions of latent representations, we present DropGraph that learns a regularization function by constructing a stand-alone graph from the backbone features. DropGraph first samples stochastic spatial feature vectors and then incorporates graph reasoning methods to generate feature map distortions. This add-on graph regularizes the network during training and can be completely skipped during inference. We provide intuitions on the linkage between graph reasoning and Dropout with further discussions on how partial graph reasoning method reduces feature correlations. To this end, we extensively study the modeling of graph vertex dependencies and the utilization of the graph for distorting backbone feature maps. DropGraph was validated on 4 tasks with a total of 8 different datasets. The experimental results show that our method outperforms other state-of-the-art regularizers while leaving the base model structure unmodified during inference.