The transformer neural network was first proposed in a 2017 paper to solve some of the issues of a simple RNN. This guide will introduce you to its operations.

Org profile for Mosaic ML, Inc. on Hugging Face, the AI community building the future.

AGC CV Clips gradients based on the ratio of their norms with weights’ norms. Alibi NLP Replace attention with AliBi AugMix CV Image-preserving data augmentations BlurPool CV Applies blur before po...

The image classification precision is vastly enhanced with the growing complexity of convolutional neural network (CNN) structures. However, the uneve…

[How to Use]-[Suggested Hyperparameters]-[Technical Details]-[Attribution] Computer Vision Layer Freezing gradually makes early modules untrainable (“freezing” them), saving the cost of backpropaga...

This paper proposes an Information Bottleneck theory based filter pruning method that uses a statistical measure called Mutual Information (MI). The MI between filters and class labels, also called \textit{Relevance}, is computed using the filter's activation maps and the annotations. The filters having High Relevance (HRel) are considered to be more important. Consequently, the least important filters, which have lower Mutual Information with the class labels, are pruned. Unlike the existing MI based pruning methods, the proposed method determines the significance of the filters purely based on their corresponding activation map's relationship with the class labels. Architectures such as LeNet-5, VGG-16, ResNet-56\textcolor{myblue}{, ResNet-110 and ResNet-50 are utilized to demonstrate the efficacy of the proposed pruning method over MNIST, CIFAR-10 and ImageNet datasets. The proposed method shows the state-of-the-art pruning results for LeNet-5, VGG-16, ResNet-56, ResNet-110 and ResNet-50 architectures. In the experiments, we prune 97.98 \%, 84.85 \%, 76.89\%, 76.95\%, and 63.99\% of Floating Point Operation (FLOP)s from LeNet-5, VGG-16, ResNet-56, ResNet-110, and ResNet-50 respectively.} The proposed HRel pruning method outperforms recent state-of-the-art filter pruning methods. Even after pruning the filters from convolutional layers of LeNet-5 drastically (i.e. from 20, 50 to 2, 3, respectively), only a small accuracy drop of 0.52\% is observed. Notably, for VGG-16, 94.98\% parameters are reduced, only with a drop of 0.36\% in top-1 accuracy. \textcolor{myblue}{ResNet-50 has shown a 1.17\% drop in the top-5 accuracy after pruning 66.42\% of the FLOPs.} In addition to pruning, the Information Plane dynamics of Information Bottleneck theory is analyzed for various Convolutional Neural Network architectures with the effect of pruning.

The following papers by Leslie N. Smith are covered in this notebook :- A disciplined approach to neural network hyper-parameters: Part 1 – learning rate, batch size, momentum, and weight decay. paper Super-Convergence: Very Fast Training of Neural Networks Using Learning Rates. paper Exploring loss function topology with cyclical learning rates. paper Cyclical Learning Rates for Training Neural Networks. paper This notebook covers all the topics discussed with theory as well as the fasta...

Update in March 2019:After TensorFlow developers introduced the APIs of Tensorflow 2.0 on Tensorflow Dev Summit 2019, I have made my decision to turn to PyTorch. TensorFlow is a powerful open-source deep learning framework, supporting various lang

In recent years, high quality deepfake face images generated by Generative Adversarial Networks (GAN) technology have caused serious negative impacts in many fields. Traditional image forensics methods are unable to deal with deepfake that relies on powerful...

The increasingly large structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works typically ...

The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods s…

Filter pruning has been widely used for neural network compression because of its enabled practical acceleration. To date, most of the existing filter pruning works explore the importance of filters via using intra-channel information. In this paper, starting from an inter-channel perspective, we propose to perform efficient filter pruning using Channel Independence, a metric that measures the correlations among different feature maps. The less independent feature map is interpreted as containing less useful information$/$knowledge, and hence its corresponding filter can be pruned without affecting model capacity. We systematically investigate the quantification metric, measuring scheme and sensitiveness$/$reliability of channel independence in the context of filter pruning. Our evaluation results for different models on various datasets show the superior performance of our approach. Notably, on CIFAR-10 dataset our solution can bring $0.90\%$ and $0.94\%$ accuracy increase over baseline ResNet-56 and ResNet-110 models, respectively, and meanwhile the model size and FLOPs are reduced by $42.8\%$ and $47.4\%$ (for ResNet-56) and $48.3\%$ and $52.1\%$ (for ResNet-110), respectively. On ImageNet dataset, our approach can achieve $40.8\%$ and $44.8\%$ storage and computation reductions, respectively, with $0.15\%$ accuracy increase over the baseline ResNet-50 model. The code is available at https://github.com/Eclipsess/CHIP_NeurIPS2021.

The paper proposes an A-ResNet model to improve ResNet. The residual attention module with shortcut connection is introduced to enhance the focus on the target object; the dropout layer is introduced to prevent the overfitting phenomenon and improve the ...

Want an intuitive and detailed explanation of Residual Networks? Look no further! This video is an animated guide of the paper 'Deep Residual Learning for Im...

Paper tables with annotated results for How Not to Give a FLOP: Combining Regularization and Pruning for Efficient Inference

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# ResNet: Summary and Implementation

In their 2015 publication “Deep Residual Learning for Image Recognition,” Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun created a…

Download scientific diagram | Training curves for ResNet-56 and ResNet-56_P2 architectures on CIFAR-100 dataset from publication: HetConv: Beyond Homogeneous Convolution Kernels for Deep CNNs | While usage of convolutional neural networks (CNN) is widely prevalent, methods proposed so far always have considered homogeneous kernels for this task. In this paper, we propose a new type of convolution operation using heterogeneous kernels. The proposed Heterogeneous... | Convolution, Homogenization and Heterogeneity | ResearchGate, the professional network for scientists.