PB3C-CNN: An integrated PB3C and CNN based approach for plant leaf classification

CNN based music emotion classification

Music emotion recognition (MER) is usually regarded as a multi-label tagging task, and each segment of music can inspire specific emotion tags. Most researchers extract acoustic features from music and explore the relations between these features and their corresponding emotion tags. Considering the inconsistency of emotions inspired by the same music segment for human beings, seeking for the k...

Imbalanced Malware Images Classification: a CNN based Approach

Deep convolutional neural networks (CNNs) can be applied to malware binary detection through images classification. The performance, however, is degraded due to the imbalance of malware families (classes). To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final layer of deep CNNs. The original softmax loss is weighted, and the weight va...

H-CNN: Spatial Hashing Based CNN for 3D Shape Analysis

We present a novel spatial hashing based data structure to facilitate 3D shape analysis using convolutional neural networks (CNNs). Our method well utilizes the sparse occupancy of 3D shape boundary and builds hierarchical hash tables for an input model under different resolutions. Based on this data structure, we design two efficient GPU algorithms namely hash2col and col2hash so that the CNN ...

A CNN-Specific Integrated Processor

Relation Classification: CNN or RNN?

Convolutional neural networks (CNN) have delivered competitive performance on relation classification, without tedious feature engineering. A particular shortcoming of CNN, however, is that it is less powerful in modeling longspan relations. This paper presents a model based on recurrent neural networks (RNN) and compares the capabilities of CNN and RNN on the relation classification task. We c...