No Routing Needed Between Capsules | ✓ Link | 0.13 | 99.87 | 1514187 | | | | Branching/Merging CNN + Homogeneous Vector Capsules | 2020-01-24 |
Ensemble learning in CNN augmented with fully connected subnetworks | ✓ Link | 0.16 | 99.84 | | | | | EnsNet (Ensemble learning in CNN augmented with fully connected subnetworks) | 2020-03-19 |
Efficient-CapsNet: Capsule Network with Self-Attention Routing | ✓ Link | 0.16 | 99.84 | 161824 | | | | Efficient-CapsNet | 2021-01-29 |
Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods | ✓ Link | 0.17 | 99.83 | 1400000 | | | | SOPCNN (Only a single Model) | 2020-01-24 |
RMDL: Random Multimodel Deep Learning for Classification | ✓ Link | 0.18 | 99.82 | | | | | RMDL (30 RDLs) | 2018-05-03 |
Learning local discrete features in explainable-by-design convolutional neural networks | ✓ Link | 0.20 | 99.80 | 743882 | | | | R-ExplaiNet-22 (single model) | 2024-10-31 |
Regularization of Neural Networks using DropConnect | ✓ Link | 0.21 | 99.77 | | | | | DropConnect | 2013-06-13 |
Multi-column Deep Neural Networks for Image Classification | ✓ Link | 0.23 | | | | | | MCDNN | 2012-02-13 |
APAC: Augmented PAttern Classification with Neural Networks | | 0.23 | | | | | | APAC | 2015-05-13 |
Batch-normalized Maxout Network in Network | ✓ Link | 0.24 | | | | | | BNM NiN | 2015-11-09 |
Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures | ✓ Link | 0.25 | | | | | | SimpleNetv1 | 2016-08-22 |
Dynamic Routing Between Capsules | ✓ Link | 0.25 | | | | | | CapsNet | 2017-10-26 |
Training Neural Networks with Local Error Signals | ✓ Link | 0.26 | | | | | | VGG8B + LocalLearning + CO | 2019-01-20 |
SpinalNet: Deep Neural Network with Gradual Input | ✓ Link | 0.28 | 99.72 | | | | | VGG-5 (Spinal FC) | 2020-07-07 |
TextCaps : Handwritten Character Recognition with Very Small Datasets | ✓ Link | 0.29 | 99.71 | | | | | TextCaps | 2019-04-17 |
A Novel lightweight Convolutional Neural Network, ExquisiteNetV2 | ✓ Link | 0.29 | 99.71 | 518230 | | | | ExquisiteNetV2 | 2021-05-19 |
WaveMix: Resource-efficient Token Mixing for Images | ✓ Link | 0.29 | | | | | | WaveMix-128/7 | 2022-03-07 |
Fractional Max-Pooling | ✓ Link | 0.3 | | | | | | Fractional MP | 2014-12-18 |
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree | ✓ Link | 0.3 | | | | | | Tree+Max-Avg pooling | 2015-09-30 |
Competitive Multi-scale Convolution | | 0.3 | | | | | | CMsC | 2015-11-18 |
EXACT: How to Train Your Accuracy | ✓ Link | 0.33 | | | | | | EXACT (M3-CNN) | 2022-05-19 |
On Second Order Behaviour in Augmented Neural ODEs | ✓ Link | 0.37 | 99.63 | | | | | Second Order Neural Ordinary Differential Equation | 2020-06-12 |
Augmented Neural ODEs | ✓ Link | 0.37 | 99.63 | | | | | Augmented Neural Ordinary Differential Equation | 2019-04-02 |
Deeply-Supervised Nets | ✓ Link | 0.4 | | | | | | DSN | 2014-09-18 |
Convolutional Kernel Networks | | 0.4 | | | | | | CKN | 2014-06-12 |
Unsupervised Feature Learning with C-SVDDNet | | 0.4 | | | | | | C-SVDDNet | 2014-12-23 |
Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks | | 0.4 | | | | | | HOPE | 2015-02-03 |
Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network | | 0.4 | | | | | | FLSCNN | 2015-03-16 |
On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units | | 0.4 | | | | | | MIM | 2015-08-03 |
All you need is a good init | ✓ Link | 0.4 | | | | | | Fitnet-LSUV-SVM | 2015-11-19 |
Evaluating the Performance of TAAF for image classification models | ✓ Link | 0.48% | 99.52% | 421642 | 0.0188 | 35 | | TAAF-CNN | 2025-02-13 |
[]() | | 0.5 | 99.5 | 1882602 | | | | Neural Architecture Search (NAS)-enabled Convolutional Neural Network (CNN) | |
Maxout Networks | ✓ Link | 0.5 | | | | | | Maxout Networks | 2013-02-18 |
Network In Network | ✓ Link | 0.5 | | | | | | NiN | 2013-12-16 |
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks | ✓ Link | 0.5 | | | | | | ReNet | 2015-05-03 |
Deep Convolutional Neural Networks as Generic Feature Extractors | | 0.5 | | | | | | DCNN+GFE | 2017-10-06 |
Training Very Deep Networks | ✓ Link | 0.5 | | | | | | VDN | 2015-07-22 |
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data | | 0.51 | | | | | | NeuPDE | 2019-08-08 |
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization | ✓ Link | 0.53 | | | | | | Simple CNN with BaikalCMA loss | 2019-05-27 |
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision | ✓ Link | 0.58 | 99.42 | | | | | SEER (RegNet10B) | 2022-02-16 |
The Convolutional Tsetlin Machine | ✓ Link | 0.6 | 99.4 | | | | | Convolutional Tsetlin Machine | 2019-05-23 |
PCANet: A Simple Deep Learning Baseline for Image Classification? | ✓ Link | 0.6 | | | | | | PCANet | 2014-04-14 |
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and $L_0$ Regularization | ✓ Link | 0.6 | | | | | | DiffPrune (LeNet5) | 2020-12-07 |
Deep Fried Convnets | ✓ Link | 0.7 | | | | | | Deep Fried Convnets | 2014-12-22 |
Sparse Activity and Sparse Connectivity in Supervised Learning | | 0.8 | | | | | | Sparse Activity and Sparse Connectivity in Supervised Learning | 2016-03-28 |
Explaining and Harnessing Adversarial Examples | ✓ Link | 0.8 | | | | | | Explaining and Harnessing Adversarial Examples | 2014-12-20 |
BinaryConnect: Training Deep Neural Networks with binary weights during propagations | ✓ Link | 1.0 | | | | | | BinaryConnect | 2015-11-02 |
Parametric Matrix Models | | 1.01 | 98.99 | 129416 | | | | Convolutional PMM (Parametric Matrix Model) | 2024-01-22 |
Sparse Networks from Scratch: Faster Training without Losing Performance | ✓ Link | 1.26 | | | | | | LeNet 300-100 (Sparse Momentum) | 2019-07-10 |
Convolutional Clustering for Unsupervised Learning | | 1.4 | | | | | | Convolutional Clustering | 2015-11-19 |
Convolutional Sequence to Sequence Learning | ✓ Link | 1.41 | 98.59 | | | | | CNN Model by Som | 2017-05-08 |
The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses | ✓ Link | 1.5 | 98.5 | | | | | Weighted Tsetlin Machine | 2019-11-28 |
On the Ideal Number of Groups for Isometric Gradient Propagation | | 1.67 | | | | | | MLP (ideal number of groups) | 2023-02-07 |
Tensorizing Neural Networks | ✓ Link | 1.8 | 98.2 | | | | | Perceptron with a tensor train layer | 2015-09-22 |
Augmented Neural ODEs | ✓ Link | 1.8 | 98.2 | | | | | ANODE | 2019-04-02 |
The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic | ✓ Link | 1.8 | 98.2 | | | | | Tsetlin Machine | 2018-04-04 |
A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification | ✓ Link | 1.96 | 98.04 | | | | | GECCO | 2024-02-01 |
Parametric Matrix Models | | 2.62 | 97.38 | 4990 | | | | PMM (Parametric Matrix Model) | 2024-01-22 |
Trainable Activations for Image Classification | ✓ Link | 2.8 | 97.2 | 575051 | | | | DNN-5 (Trainable Activations) | 2023-01-26 |
Trainable Activations for Image Classification | ✓ Link | 3.0 | 97.0 | 386719 | | | | DNN-3 (Trainable Activations) | 2023-01-26 |
Trainable Activations for Image Classification | ✓ Link | 3.6 | 96.4 | 311651 | | | | DNN-2 (Trainable Activations) | 2023-01-26 |
Stacked What-Where Auto-encoders | ✓ Link | 4.76 | | | | | | Zhao et al. (2015) (auto-encoder) | 2015-06-08 |
ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections | | 5.0 | 95.0 | | | | | ProjectionNet | 2017-08-02 |
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems | ✓ Link | | 99.75 | | | | | µ2Net (ViT-L/16) | 2022-05-25 |
XnODR and XnIDR: Two Accurate and Fast Fully Connected Layers For Convolutional Neural Networks | ✓ Link | | 99.68 | | | | | MobileNet_XnODR | 2021-11-21 |
CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters | ✓ Link | | 99.68 | | | | | ResNet-9 | 2022-03-29 |
LR-Net: A Block-based Convolutional Neural Network for Low-Resolution Image Classification | ✓ Link | | 99.47 | | | | | LR-Net | 2022-07-19 |
Learning in Wilson-Cowan model for metapopulation | ✓ Link | | 99.31 | | | | | CNN+ Wilson-Cowan model RNN | 2024-06-24 |
Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks | ✓ Link | | 99.3 | | | | | FastSNN (CNN) | 2022-05-30 |
rKAN: Rational Kolmogorov-Arnold Networks | ✓ Link | | 99.293 | | | | | rKAN | 2024-06-20 |
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent | ✓ Link | | 99.27 | | | | | CNN-5 Layer | 2021-06-16 |
fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions | ✓ Link | | 99.228 | | | | | fKAN | 2024-06-11 |
Spike time displacement based error backpropagation in convolutional spiking neural networks | | | 99.2 | | | | | StiDi-BP in R-CSNN | 2021-08-31 |
Learning in Wilson-Cowan model for metapopulation | ✓ Link | | 98.13 | | | | | Wilson-Cowan model RNN | 2024-06-24 |
Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines | | | 98.13 | | | | | Hypervector Tsetlin Machine | 2024-06-04 |
[]() | | | 98.03 | 1208586 | | | | ViT-Mini_D9 | |
Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks | ✓ Link | | 97.91 | | | | | FastSNN (MLP) | 2022-05-30 |
The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware | ✓ Link | | 96.2 | | | | | Binarized MLP with on-chip spiking backpropagation (on Loihi) | 2021-06-13 |
Improving k-Means Clustering Performance with Disentangled Internal Representations | ✓ Link | | 95.5 | | | | | SNNL-5 | 2020-06-05 |
Personalized Federated Learning with Hidden Information on Personalized Prior | | | 92.47 | | | | | pFedBreD_ns_mg | 2022-11-19 |
Performance of Gaussian Mixture Model Classifiers on Embedded Feature Spaces | ✓ Link | | | | | | 70 | DGMMC-S | 2024-10-17 |