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Found 9 papers, 1 papers with code
A Recipe for Efficient SBIR Models: Combining Relative Triplet Loss with Batch Normalization and Knowledge Distillation
Then, we introduce a Relative Triplet Loss (RTL), an adapted triplet loss to overcome those limitations through loss weighting based on anchors similarity.
Induced Feature Selection by Structured Pruning
The advent of sparsity inducing techniques in neural networks has been of a great help in the last few years.
FasterAI: A Lightweight Library for Creating Sparse Neural Networks
FasterAI is a PyTorch-based library, aiming to facilitate the utilization of deep neural networks compression techniques such as sparsification, pruning, knowledge distillation, or regularization.
Improve Convolutional Neural Network Pruning by Maximizing Filter Variety
This technique ensures that the criteria of selection focuses on redundant filters, while retaining the rare ones, thus maximizing the variety of remaining filters.
Where Is My Mind (looking at)? Predicting Visual Attention from Brain Activity
The codes and dataset considered in this paper have been made available at \url{https://figshare. com/s/3e353bd1c621962888ad} to promote research in the field.
Towards Lightweight Neural Animation : Exploration of Neural Network Pruning in Mixture of Experts-based Animation Models
Their motion is synthesized by a neural network.
An Experimental Study of the Impact of Pre-training on the Pruning of a Convolutional Neural Network
Neural networks usually involve a large number of parameters, which correspond to the weights of the network.
One-Cycle Pruning: Pruning ConvNets Under a Tight Training Budget
Most of the time, sparsity is introduced using a three-stage pipeline: 1) train the model to convergence, 2) prune the model according to some criterion, 3) fine-tune the pruned model to recover performance.
Modulated Self-attention Convolutional Network for VQA
As new data-sets for real-world visual reasoning and compositional question answering are emerging, it might be needed to use the visual feature extraction as a end-to-end process during training.
