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Found 8 papers, 5 papers with code
Target Aware Network Architecture Search and Compression for Efficient Knowledge Transfer
Later, in the second stage, the redundant filters are pruned from the fine-tuned CNN to decrease the network's complexity for the target task while preserving the performance.
AdaInject: Injection Based Adaptive Gradient Descent Optimizers for Convolutional Neural Networks
Overall, we observe very promising performance improvement of existing optimizers with the proposed AdaInject approach.
Deep Model Compression based on the Training History
First, we train the model and select the filter pairs with redundant filters in each pair.
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning
The experimental results obtained in this study depict that tuning of the pre-trained CNN layers with the knowledge from the target dataset confesses better transfer learning ability.
An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos
Traditionally in deep learning based human activity recognition approaches, either a few random frames or every $k^{th}$ frame of the video is considered for training the 3D CNN, where $k$ is a small positive integer, like 4, 5, or 6.
AutoFCL: Automatically Tuning Fully Connected Layers for Handling Small Dataset
Fine-tuning the newly learned (target-dependent) FC layers leads to state-of-the-art performance, according to the experiments carried out in this research.
Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification
To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets.
RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification
The results of the proposed RCCNet model are compared with five state-of-the-art CNN models in terms of the accuracy, weighted average F1 score and training time.
