| ResNet20 with reSGHMC |
- Percentage correct
- 94.62
|
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC 2020-08-12 |
코드 1 · 코드 2 |
| PDO-eConv (p6m,0.37M) |
- Percentage correct
- 94.62
|
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions 2020-07-20 |
코드 1 · 코드 2 · 코드 3 |
| Evolution |
- Percentage correct
- 94.6
|
Large-Scale Evolution of Image Classifiers 2017-03-03 |
코드 1 · 코드 2 |
| RL+NT |
- Percentage correct
- 94.6
|
Efficient Architecture Search by Network Transformation 2017-07-16 |
코드 1 · 코드 2 · 코드 3 |
| Convolutional Performer for Vision (CPV) |
- Percentage correct
- 94.46
|
Convolutional Xformers for Vision 2022-01-25 |
코드 1 |
| PreResNet-110 |
- Percentage correct
- 94.4367
|
How to Use Dropout Correctly on Residual Networks with Batch Normalization 2023-02-13 |
코드 1 |
| ResNet+ELU |
- Percentage correct
- 94.4
|
Deep Residual Networks with Exponential Linear Unit 2016-04-14 |
코드 1 |
| Deep Complex |
- Percentage correct
- 94.4
|
Deep Complex Networks 2017-05-27 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 |
| PDO-eConv (p6,0.36M) |
- Percentage correct
- 94.35
|
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions 2020-07-20 |
코드 1 · 코드 2 · 코드 3 |
| Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods |
- Percentage correct
- 94.29
|
Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods 2020-01-24 |
코드 1 |
| Fitnet4-LSUV |
- Percentage correct
- 94.2
|
All you need is a good init 2015-11-19 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 · 코드 10 · 코드 11 |
| R-ExplaiNet-26 |
- Percentage correct
- 94.15
- Parameters
- 0.89 M
|
Learning local discrete features in explainable-by-design convolutional neural networks 2024-10-31 |
코드 1 |
| ABNet-2G-R0 |
- Percentage correct
- 94.118
|
ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel Realities 2024-11-28 |
코드 1 |
| ResNet 9 + Mish |
- Percentage correct
- 94.05
|
Mish: A Self Regularized Non-Monotonic Activation Function 2019-08-23 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 |
| Tree+Max-Avg pooling |
- Percentage correct
- 94.0
|
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree 2015-09-30 |
코드 1 · 코드 2 |
| Beta-Rank |
- Percentage correct
- 93.97
|
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis 2023-04-15 |
코드 1 |
| ResNet-110 (SAP) |
- Percentage correct
- 93.861
|
Stochastic Subsampling With Average Pooling 2024-09-25 |
연결 없음 |
| SA quadratic embedding |
- Percentage correct
- 93.8
|
On the Relationship between Self-Attention and Convolutional Layers 2019-11-08 |
코드 1 |
| kEffNet-B0 32ch |
- Percentage correct
- 93.75
|
Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks 2022-06-30 |
코드 1 · 코드 2 |
| OTTT |
- Percentage correct
- 93.73
|
Online Training Through Time for Spiking Neural Networks 2022-10-09 |
코드 1 |
| SSCNN |
- Percentage correct
- 93.7
|
Spatially-sparse convolutional neural networks 2014-09-22 |
코드 1 · 코드 2 · 코드 3 |
| NNCLR |
- Percentage correct
- 93.7
|
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations 2021-04-29 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| Tuned CNN |
- Percentage correct
- 93.6
|
Scalable Bayesian Optimization Using Deep Neural Networks 2015-02-19 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| Exponential Linear Units |
- Percentage correct
- 93.5
|
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) 2015-11-23 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 · 코드 10 · 코드 11 · 코드 12 · 코드 13 · 코드 14 · 코드 15 · 코드 16 |
| BNM NiN |
- Percentage correct
- 93.3
|
Batch-normalized Maxout Network in Network 2015-11-09 |
코드 1 |
| Universum Prescription |
- Percentage correct
- 93.3
|
Universum Prescription: Regularization using Unlabeled Data 2015-11-11 |
연결 없음 |
| CMsC |
- Percentage correct
- 93.1
|
Competitive Multi-scale Convolution 2015-11-18 |
연결 없음 |
| DGPPF-ResNet18 |
- Percentage correct
- 92.90
|
Distilled Gradual Pruning with Pruned Fine-tuning 2024-02-15 |
코드 1 |
| kMobileNet V3 Large 16ch |
- Percentage correct
- 92.74
|
Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks 2022-06-30 |
코드 1 · 코드 2 |
| NiN+APL |
- Percentage correct
- 92.5
|
Learning Activation Functions to Improve Deep Neural Networks 2014-12-21 |
코드 1 · 코드 2 · 코드 3 |