Using Deep Learning for Image-Based Plant Disease Detection

11 Apr 20164 code implementations

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

20 Jun 20141 code implementation

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.

DIMENSIONALITY REDUCTION

Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

20 May 20162 code implementations

Ristretto simulates the hardware arithmetic of a custom hardware accelerator.

Energy and Policy Considerations for Deep Learning in NLP

ACL 2019 2 code implementations

Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data.

SeesawFaceNets: sparse and robust face verification model for mobile platform

arXiv 2019 5 code implementations

Therefore, designing lightweight networks with low memory requirement and computational cost is one of the most practical solutions for face verification on mobile platform.

FACE RECOGNITION FACE VERIFICATION

Submanifold Sparse Convolutional Networks

5 Jun 20172 code implementations

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)

3D PART SEGMENTATION

Dynamic Graph CNN for Learning on Point Clouds

24 Jan 201811 code implementations

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

CVPR 2019 2 code implementations

We propose a new supervized learning framework for oversegmenting 3D point clouds into superpoints.

METRIC LEARNING SEMANTIC SEGMENTATION

SegMap: 3D Segment Mapping using Data-Driven Descriptors

25 Apr 20181 code implementation

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.