Models from experiments referenced in the paper "Training CNNs with Low-Rank Filters for Efficient Image Classification", https://arxiv.org/abs/1511.06744 Model names differ from those in the paper, but the csv files for each set of experiments relates the paper's name for the model and the real name of the model here: cifarma.csv: Network-in-Network CIFAR10 Models mitma.csv: MIT Places Models googlenetma.csv: GoogLeNet ILSVRC2012 Models vggma.csv: VGG-11 ILSVRC2012 Model
Models to run the CNN model. The model named cell_seg_model_001.pth is the pre-trained model by Sche...
Computer vision has become increasingly important and effective in recent years due to its wide-rang...
The aim of the research is to compare traditional and deep learning methods in image classification ...
Models from experiments referenced in the paper "Training CNNs with Low-Rank Filters for Efficient I...
We propose a new method for creating computationally efficient convolutional neural networks (CNNs) ...
This is the poster presented at ICLR 2016 for the paper accepted to the conference track, Training C...
We propose a new method for creating computationally efficient convolutional neural networks (CNNs) ...
This is fully trained image classification convolutional neural network (CNN). We used a pre-traine...
Transfer learning (TL) has been widely utilized to address the lack of training data for deep learni...
There are various ways a user can go about selecting a Convolutional Neural Net- work model for the...
A diverse database of over 1.4B 3x3 convolution filters extracted from CNN models trained for variou...
CNN Filter DB: An Empirical Investigation of Trained Convolutional. Poster as presented at CVPR2022...
Currently, many theoretical as well as practically relevant questions towards the transferability an...
Pre-trained network weights to reproduce the results shown in the paper "A Note on the Regularity of...
With the introduction of Convolutional Neural Networks, models for image classification achieve high...
Models to run the CNN model. The model named cell_seg_model_001.pth is the pre-trained model by Sche...
Computer vision has become increasingly important and effective in recent years due to its wide-rang...
The aim of the research is to compare traditional and deep learning methods in image classification ...
Models from experiments referenced in the paper "Training CNNs with Low-Rank Filters for Efficient I...
We propose a new method for creating computationally efficient convolutional neural networks (CNNs) ...
This is the poster presented at ICLR 2016 for the paper accepted to the conference track, Training C...
We propose a new method for creating computationally efficient convolutional neural networks (CNNs) ...
This is fully trained image classification convolutional neural network (CNN). We used a pre-traine...
Transfer learning (TL) has been widely utilized to address the lack of training data for deep learni...
There are various ways a user can go about selecting a Convolutional Neural Net- work model for the...
A diverse database of over 1.4B 3x3 convolution filters extracted from CNN models trained for variou...
CNN Filter DB: An Empirical Investigation of Trained Convolutional. Poster as presented at CVPR2022...
Currently, many theoretical as well as practically relevant questions towards the transferability an...
Pre-trained network weights to reproduce the results shown in the paper "A Note on the Regularity of...
With the introduction of Convolutional Neural Networks, models for image classification achieve high...
Models to run the CNN model. The model named cell_seg_model_001.pth is the pre-trained model by Sche...
Computer vision has become increasingly important and effective in recent years due to its wide-rang...
The aim of the research is to compare traditional and deep learning methods in image classification ...