kFsN

kFsN, short for k-Feature Sparse Network, is a class of artificial neural networks designed to impose structured sparsity on hidden representations. In a kFsN, each layer outputs a sparse vector by keeping only the k features with the highest activation values for each input example; the remaining activations are set to zero. The value of k is a hyperparameter and can be fixed or scheduled during training.

Implementation approaches include hard top-k masking, where a binary mask is applied to the activations, and differentiable approximations such as soft-k or gumbel-softmax to preserve gradient flow. Training often employs straight-through estimators or surrogate losses to approximate the non-differentiable masking, and may incorporate regularization to encourage smaller or more stable k across layers.

The concept arises from broader efforts to reduce computation and memory requirements in neural networks while exploring the regularizing effects of sparsity. kFsN can be applied to feedforward and recurrent architectures and has been considered in contexts such as image classification and natural language processing, particularly where deployment on resource-constrained hardware is a priority.

Advantages of kFsN include reduced arithmetic operations and memory usage at inference time, potential improvements in energy efficiency, and the possibility of enhanced interpretability through sparser representations. Limitations include sensitivity to the choice of k, potential degradation in accuracy for tasks requiring dense feature interactions, and varying compatibility with different architectural designs.

Related concepts encompass sparse neural networks, pruning methods, and activation-based sparsification techniques such as top-k pooling and sparsemax.