Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.
& Darrell, T
9 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU ($\approx$ 2.5 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments. Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.
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representative citing papers
A u-shaped fully-convolutional encoder-decoder with skip connections trained with elastic-deformation augmentation produces accurate biomedical image segmentations from very small training sets.
PyTorch demonstrates compatibility of imperative Pythonic usability with high performance and accelerator support through its runtime architecture and benchmark results.
TextGrad performs automatic differentiation for compound AI systems by backpropagating natural-language feedback from LLMs to optimize variables ranging from code to molecular structures.
Mixed precision training uses FP16 for most computations, FP32 master weights for accumulation, and loss scaling to enable accurate training of large DNNs with halved memory usage.
MobileNets introduce depthwise separable convolutions plus width and resolution multipliers to produce efficient CNNs that trade off latency and accuracy for mobile and embedded vision applications.
A pruning-quantization-Huffman pipeline compresses deep neural networks 35-49x without accuracy loss.
Siamese NN trained on ImageNet pairs yields 0.94 mAP retrieval and 0.83 mAP pattern spotting (IoU=0.7) on the Tobacco800 document collection.
A survey introducing Bayesian Neural Networks and comparing approximate inference methods to enable uncertainty quantification in neural network predictions.
citing papers explorer
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Deep Residual Learning for Image Recognition
Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.
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U-Net: Convolutional Networks for Biomedical Image Segmentation
A u-shaped fully-convolutional encoder-decoder with skip connections trained with elastic-deformation augmentation produces accurate biomedical image segmentations from very small training sets.
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch demonstrates compatibility of imperative Pythonic usability with high performance and accelerator support through its runtime architecture and benchmark results.
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TextGrad: Automatic "Differentiation" via Text
TextGrad performs automatic differentiation for compound AI systems by backpropagating natural-language feedback from LLMs to optimize variables ranging from code to molecular structures.
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Mixed Precision Training
Mixed precision training uses FP16 for most computations, FP32 master weights for accumulation, and loss scaling to enable accurate training of large DNNs with halved memory usage.
-
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
MobileNets introduce depthwise separable convolutions plus width and resolution multipliers to produce efficient CNNs that trade off latency and accuracy for mobile and embedded vision applications.
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Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
A pruning-quantization-Huffman pipeline compresses deep neural networks 35-49x without accuracy loss.
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Image Retrieval and Pattern Spotting using Siamese Neural Network
Siamese NN trained on ImageNet pairs yields 0.94 mAP retrieval and 0.83 mAP pattern spotting (IoU=0.7) on the Tobacco800 document collection.
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Bayesian Neural Networks: An Introduction and Survey
A survey introducing Bayesian Neural Networks and comparing approximate inference methods to enable uncertainty quantification in neural network predictions.