General Classification
3931 papers with code • 11 benchmarks • 8 datasets
Algorithms trying to solve the general task of classification.
Benchmarks
These leaderboards are used to track progress in General Classification
Libraries
Use these libraries to find General Classification models and implementations
MNIST
Fashion-MNIST
Adult
iris
Hyper-Kvasir Dataset
Wine
Kvasir-Capsule
CVR
Most implemented papers
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
tensorpack/tensorpack
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CVPR 2018
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e. g., 10-150 MFLOPs).
Domain-Adversarial Training of Neural Networks
Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
open-mmlab/mmaction2
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CVPR 2017
The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks.
EMNIST: an extension of MNIST to handwritten letters
Chizuchizu/amplify-hackathon
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The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems.
In Defense of the Triplet Loss for Person Re-Identification
layumi/Person_reID_baseline_pytorch
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In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning.
CBAM: Convolutional Block Attention Module
xmu-xiaoma666/External-Attention-pytorch
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ECCV 2018
We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks.
Character-level Convolutional Networks for Text Classification
makcedward/nlpaug
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NeurIPS 2015
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification.
Adversarial Autoencoders
eriklindernoren/PyTorch-GAN
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In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost.
Modeling Relational Data with Graph Convolutional Networks
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
