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Using Deep Learning for Image-Based Plant Disease Detection
When testing the model on a set of images collected from trusted online sources - i. e. taken under conditions different from the images used for training - the model still achieves an accuracy of 31. 4%.
Caffe: Convolutional Architecture for Fast Feature Embedding
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.
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
What will happen if we increase the dataset size by 10x or 100x?
#20 best model for
Semantic Segmentation
on PASCAL VOC 2012 test
IMAGE CLASSIFICATION OBJECT DETECTION POSE ESTIMATION REPRESENTATION LEARNING SEMANTIC SEGMENTATION
Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks
Ristretto simulates the hardware arithmetic of a custom hardware accelerator.
Energy and Policy Considerations for Deep Learning in NLP
Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data.
Submanifold Sparse Convolutional Networks
Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc.
#2 best model for
3D Part Segmentation
on ShapeNet-Part
(Instance Average IoU metric)
Dynamic Graph CNN for Learning on Point Clouds
Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices.
Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning
We propose a new supervized learning framework for oversegmenting 3D point clouds into superpoints.
SegMap: 3D Segment Mapping using Data-Driven Descriptors
While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information.
PointConv: Deep Convolutional Networks on 3D Point Clouds
Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.