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Recently, the proposed deep MLP models have stirred up a lot of interest in the vision community.
Mmdetection: Open mmlab detection toolbox and benchmark
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Observations on the scratch-reflex in the spinal dog
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A logical calculus of the ideas immanent in nervous activity
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The perceptron: a probabilistic model for information storage and organization in the brain
Rosenblatt F · 1958
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Perceptrons. 1969;
Minsky M, Papert S · 1969
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Neural network model for a mechanism of pattern recognition unaffected by shift in position-neocognitron
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Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
Fukushima K, Miyake S · 1982
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Neocognitron: A neural network model for a mechanism of visual pattern recognition
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Learning internal representations by error propagation
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A learning algorithm for boltzmann machines
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Information processing in dynamical systems: Foundations of harmony theory
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Learning and relearning in boltzmann machines
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Backpropagation applied to handwritten zip code recognition
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Approximation by superpositions of a sigmoidal function
Cybenko G · 1989
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Multilayer feedforward networks are universal approximators
Hornik K, Stinchcombe MB, White H · 1989
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On the approximate realization of continuous mappings by neural networks
Funahashi K · 1989
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Deterministic boltzmann learning performs steepest descent in weight-space
Hinton GE · 1989
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Gradient-based learning applied to document recognition
LeCun Y, Bottou L, Bengio Y, Haffner P · 1998
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Mean-field theory of boltzmann machine learning
Tanaka T · 1998
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Cramming more components onto integrated circuits
Moore GE · 1998
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Improved baselines with momentum contrastive learning
Chen X, Fan H, Girshick RB, He K · 2003
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Resnest: Split-attention networks
Zhang H, Wu C, Zhang Z, Zhu Y, Zhang Z, Lin H, et al · 2004
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Reducing the dimensionality of data with neural networks
Hinton GE, Salakhutdinov RR · 2006
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New millennium AI and the convergence of history
Schmidhuber J · 2007
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Scalable parallel programming with cuda: Is cuda the parallel programming model that application developers have been waiting for?
Nickolls J, Buck I, Garland M, Skadron K · 2008
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NVIDIA tesla: A unified graphics and computing architecture
Lindholm E, Nickolls J, Oberman SF, Montrym J · 2008
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Imagenet: A large-scale hierarchical image database
Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L · 2009
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky A, Sutskever I, Hinton GE · 2012
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Stochastic gradient descent tricks
Bottou L · 2012
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Learning a deep convolutional network for image super-resolution
Dong C, Loy CC, He K, Tang X · 2014
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Microsoft COCO: common objects in context
Lin T, Maire M, Belongie SJ, Hays J, Perona P, Ramanan D, et al · 2014
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Faster R-CNN: towards real-time object detection with region proposal networks
Ren S, He K, Girshick RB, Sun J · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger O, Fischer P, Brox T · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan K, Zisserman A · 2015
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Accelerating the super-resolution convolutional neural network
Dong C, Loy CC, Tang X · 2016
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Bridging nonlinearities and stochastic regularizers with gaussian error linear units
Hendrycks D, Gimpel K · 2016
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Ba LJ, Kiros JR, Hinton GE · 2016
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The cityscapes dataset for semantic urban scene understanding
Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, et al · 2016
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Multi-scale context aggregation by dilated convolutions
Yu F, Koltun V · 2016
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Deep residual learning for image recognition
He K, Zhang X, Ren S, Sun J · 2016
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Xception: Deep learning with depthwise separable convolutions
Chollet F · 2017
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Mask R-CNN
He K, Gkioxari G, Dollár P, Girshick RB · 2017
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Deep multi-scale convolutional neural network for dynamic scene deblurring
Nah S, Kim TH, Lee KM · 2017
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Attention is all you need
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al · 2017
Cited alongside, same era.
Revisiting unreasonable effectiveness of data in deep learning era
Sun C, Shrivastava A, Singh S, Gupta A · 2017
Cited alongside, same era.
Deformable convolutional networks
Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, et al · 2017
Cited alongside, same era.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer N, Mirhoseini A, Maziarz K, Davis A, Le QV, Hinton GE, et al · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Lin T, Dollár P, Girshick RB, He K, Hariharan B, Belongie SJ · 2017
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA · 2017
Cited alongside, same era.
Jittor-mlp
Liu R · 2021
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Vision permutator: A permutable mlp-like architecture for visual recognition
Hou Q, Jiang Z, Yuan L, Cheng M, Yan S, Feng J · 2021
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Raftmlp: Do mlp-based models dream of winning over computer vision?
Tatsunami Y, Taki M · 2021
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AS-MLP: an axial shifted MLP architecture for vision
Lian D, Yu Z, Sun X, Gao S · 2021
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Sparse-mlp: A fully-mlp architecture with conditional computation
Lou Y, Xue F, Zheng Z, You Y · 2021
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Aggregated residual transformations for deep neural networks
Xie S, Girshick RB, Dollár P, Tu Z, He K · 2017
Cited alongside, same era.
Scene parsing through ADE20K dataset
Zhou B, Zhao H, Puig X, Fidler S, Barriuso A, Torralba A · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu J, Park T, Isola P, Efros AA · 2017
Cited alongside, same era.
Quo vadis, action recognition? A new model and the kinetics dataset
Carreira J, Zisserman A · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi CR, Su H, Mo K, Guibas LJ · 2017
Cited alongside, same era.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi CR, Yi L, Su H, Guibas LJ · 2017
Cited alongside, same era.
Riquelme C, Puigcerver J, Mustafa B, Neumann M, Jenatton R, Pinto AS, et al · 2021
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Repmlpnet: Hierarchical vision MLP with re-parameterized locality
Ding X, Chen H, Zhang X, Han J, Ding G · 2021
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Convmlp: Hierarchical convolutional mlps for vision
Li J, Hassani A, Walton S, Shi H · 2021
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Less is more: Pay less attention in vision transformers
Pan Z, Zhuang B, He H, Liu J, Cai J · 2021
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Resnet strikes back: An improved training procedure in timm
Wightman R, Touvron H, Jégou H · 2021
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Efficientnetv2: Smaller models and faster training
Tan M, Le QV · 2021
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Polyloss: A polynomial expansion perspective of classification loss functions
Leng Z, Tan M, Liu C, Cubuk ED, Shi J, Cheng S, et al · 2021
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Meta pseudo labels
Pham H, Dai Z, Xie Q, Le QV · 2021
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Training data-efficient image transformers & distillation through attention
Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, Jégou H · 2021
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Tokens-to-token vit: Training vision transformers from scratch on imagenet
Yuan L, Chen Y, Wang T, Yu W, Shi Y, Jiang Z, et al · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wang W, Xie E, Li X, Fan D, Song K, Liang D, et al · 2021
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Going deeper with image transformers
Touvron H, Cord M, Sablayrolles A, Synnaeve G, Jégou H · 2021
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Twins: Revisiting the design of spatial attention in vision transformers
Chu X, Tian Z, Wang Y, Zhang B, Ren H, Wei X, et al · 2021
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Shuffle transformer: Rethinking spatial shuffle for vision transformer
Huang Z, Ben Y, Luo G, Cheng P, Yu G, Fu B · 2021
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Coatnet: Marrying convolution and attention for all data sizes
Dai Z, Liu H, Le QV, Tan M · 2021
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Beit: BERT pre-training of image transformers
Bao H, Dong L, Wei F · 2021
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Mixergan: An mlp-based architecture for unpaired image-to-image translation
Cazenavette G, Guevara MLD · 2021
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Pointmixer: Mlp-mixer for point cloud understanding
Choe J, Park C, Rameau F, Park J, Kweon IS · 2021
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Point transformer
Zhao H, Jiang L, Jia J, Torr PHS, Koltun V · 2021
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Simmim: A simple framework for masked image modeling
Xie Z, Zhang Z, Cao Y, Lin Y, Bao J, Yao Z, et al · 2021
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Masked autoencoders are scalable vision learners
He K, Chen X, Xie S, Li Y, Dollár P, Girshick RB · 2021
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Masked feature prediction for self-supervised visual pre-training
Wei C, Fan H, Xie S, Wu C, Yuille AL, Feichtenhofer C · 2021
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UNETR: transformers for 3d medical image segmentation
Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman BA, et al · 2022
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Dynamixer: A vision MLP architecture with dynamic mixing
Wang Z, Jiang W, Zhu Y, Yuan L, Song Y, Liu W · 2022
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S 2 {}^{\mbox{2}} -mlp: Spatial-shift MLP architecture for vision
Yu T, Li X, Cai Y, Sun M, Li P · 2022
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CycleMLP: A MLP-like architecture for dense prediction
Chen S, Xie E, GE C, Chen R, Liang D, Luo P · 2022
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Activemlp: An mlp-like architecture with active token mixer
Wei G, Zhang Z, Lan C, Lu Y, Chen Z · 2022
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Mixing and shifting: Exploiting global and local dependencies in vision mlps
Zheng H, He P, Chen W, Zhou M · 2022
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Liu Z, Mao H, Wu C, Feichtenhofer C, Darrell T, Xie S · 2022
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Guo M, Lu C, Liu Z, Cheng M, Hu S · 2022
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Scaling up your kernels to 31x31: Revisiting large kernel design in cnns
Ding X, Zhang X, Zhou Y, Han J, Ding G, Sun J · 2022
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Pyramidtnt: Improved transformer-in-transformer baselines with pyramid architecture
Han K, Guo J, Tang Y, Wang Y · 2022
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MAXIM: multi-axis MLP for image processing
Tu Z, Talebi H, Zhang H, Yang F, Milanfar P, Bovik A, et al · 2022
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Skating-mixer: Multimodal mlp for scoring figure skating
Xia J, Zhuge M, Geng T, Fan S, Wei Y, He Z, et al · 2022
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Rethinking network design and local geometry in point cloud: A simple residual MLP framework
Ma X, Qin C, You H, Ran H, Fu Y · 2022
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Unext: Mlp-based rapid medical image segmentation network
Valanarasu JMJ, Patel VM · 2022
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Vitaev2: Vision transformer advanced by exploring inductive bias for image recognition and beyond
Zhang Q, Xu Y, Zhang J, Tao D · 2022
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Gga-mlp: A greedy genetic algorithm to optimize weights and biases in multilayer perceptron
Bansal P, Lamba R, Jain V, Jain T, Shokeen S, Kumar S, et al · 2022
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