MDMLP: Image Classification from Scratch on Small Datasets with MLP
- URL: http://arxiv.org/abs/2205.14477v1
- Date: Sat, 28 May 2022 16:26:59 GMT
- Title: MDMLP: Image Classification from Scratch on Small Datasets with MLP
- Authors: Tian Lv, Chongyang Bai, Chaojie Wang
- Abstract summary: Recently, the attention mechanism has become a go-to technique for natural language processing and computer vision tasks.
Recently, theMixer and other-based architectures, based simply on multi-layer perceptrons (MLPs), are also powerful compared to CNNs and attention techniques.
- Score: 7.672827879118106
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The attention mechanism has become a go-to technique for natural language
processing and computer vision tasks. Recently, the MLP-Mixer and other
MLP-based architectures, based simply on multi-layer perceptrons (MLPs), are
also powerful compared to CNNs and attention techniques and raises a new
research direction. However, the high capability of the MLP-based networks
severely relies on large volume of training data, and lacks of explanation
ability compared to the Vision Transformer (ViT) or ConvNets. When trained on
small datasets, they usually achieved inferior results than ConvNets. To
resolve it, we present (i) multi-dimensional MLP (MDMLP), a conceptually simple
and lightweight MLP-based architecture yet achieves SOTA when training from
scratch on small-size datasets; (ii) multi-dimension MLP Attention Tool
(MDAttnTool), a novel and efficient attention mechanism based on MLPs. Even
without strong data augmentation, MDMLP achieves 90.90% accuracy on CIFAR10
with only 0.3M parameters, while the well-known MLP-Mixer achieves 85.45% with
17.1M parameters. In addition, the lightweight MDAttnTool highlights objects in
images, indicating its explanation power. Our code is available at
https://github.com/Amoza-Theodore/MDMLP.
Related papers
- R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition [33.53114929452528]
Vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community.
We present an achieves a view-based 3D object recognition task by considering the communications between patches from different views.
With a conceptually simple structure, our R$2$MLP achieves competitive performance compared with existing methods.
arXiv Detail & Related papers (2022-11-20T21:13:02Z) - MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing [123.43419144051703]
We present a novel-like 3D architecture for video recognition.
The results are comparable to state-of-the-art widely-used 3D CNNs and video.
arXiv Detail & Related papers (2022-06-13T16:21:33Z) - Mixing and Shifting: Exploiting Global and Local Dependencies in Vision
MLPs [84.3235981545673]
Token-mixing multi-layer perceptron (MLP) models have shown competitive performance in computer vision tasks.
We present Mix-Shift-MLP which makes the size of the local receptive field used for mixing increase with respect to the amount of spatial shifting.
MS-MLP achieves competitive performance in multiple vision benchmarks.
arXiv Detail & Related papers (2022-02-14T06:53:48Z) - Sparse MLP for Image Recognition: Is Self-Attention Really Necessary? [65.37917850059017]
We build an attention-free network called sMLPNet.
For 2D image tokens, sMLP applies 1D along the axial directions and the parameters are shared among rows or columns.
When scaling up to 66M parameters, sMLPNet achieves 83.4% top-1 accuracy, which is on par with the state-of-the-art Swin Transformer.
arXiv Detail & Related papers (2021-09-12T04:05:15Z) - ConvMLP: Hierarchical Convolutional MLPs for Vision [7.874749885641495]
We propose a hierarchical ConMLP: a light-weight, stage-wise, co-design for visual recognition.
We show that ConvMLP can be seamlessly transferred and achieve competitive results with fewer parameters.
arXiv Detail & Related papers (2021-09-09T17:52:57Z) - Hire-MLP: Vision MLP via Hierarchical Rearrangement [58.33383667626998]
Hire-MLP is a simple yet competitive vision architecture via rearrangement.
The proposed Hire-MLP architecture is built with simple channel-mixing operations, thus enjoys high flexibility and inference speed.
Experiments show that our Hire-MLP achieves state-of-the-art performance on the ImageNet-1K benchmark.
arXiv Detail & Related papers (2021-08-30T16:11:04Z) - CycleMLP: A MLP-like Architecture for Dense Prediction [26.74203747156439]
CycleMLP is a versatile backbone for visual recognition and dense predictions.
It can cope with various image sizes and achieves linear computational complexity to image size by using local windows.
CycleMLP aims to provide a competitive baseline on object detection, instance segmentation, and semantic segmentation for models.
arXiv Detail & Related papers (2021-07-21T17:23:06Z) - AS-MLP: An Axial Shifted MLP Architecture for Vision [50.11765148947432]
An Axial Shifted architecture (AS-MLP) is proposed in this paper.
By axially shifting channels of the feature map, AS-MLP is able to obtain the information flow from different directions.
With the proposed AS-MLP architecture, our model obtains 83.3% Top-1 accuracy with 88M parameters and 15.2 GFLOPs on the ImageNet-1K dataset.
arXiv Detail & Related papers (2021-07-18T08:56:34Z) - S$^2$-MLP: Spatial-Shift MLP Architecture for Vision [34.47616917228978]
Recently, visual Transformer (ViT) and its following works abandon the convolution and exploit the self-attention operation.
In this paper, we propose a novel pure architecture, spatial-shift (S$2$-MLP)
arXiv Detail & Related papers (2021-06-14T15:05:11Z) - MLP-Mixer: An all-MLP Architecture for Vision [93.16118698071993]
We present-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs).
Mixer attains competitive scores on image classification benchmarks, with pre-training and inference comparable to state-of-the-art models.
arXiv Detail & Related papers (2021-05-04T16:17:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.