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Rotated MNIST
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
GDO: Gradual Domain Osmosis
On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory
Unsupervised discovery of the shared and private geometry in multi-view data
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding
Progressive Conservative Adaptation for Evolving Target Domains
Scale-Rotation-Equivariant Lie Group Convolution Neural Networks (Lie Group-CNNs)
Group Invariant Global Pooling
Artificial Neuronal Ensembles with Learned Context Dependent Gating
Learning unfolded networks with a cyclic group structure
Learning Invariant Representations for Equivariant Neural Networks Using Orthogonal Moments
Diversity Boosted Learning for Domain Generalization with Large Number of Domains
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge
Transform-Invariant Convolutional Neural Networks for Image Classification and Search
Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration
Learning Augmentation Distributions using Transformed Risk Minimization
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
Learning Partial Equivariances from Data
Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction
CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Domain Generalization using Causal Matching
Invariant Integration in Deep Convolutional Feature Space
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring In Data
Visual Context-aware Convolution Filters for Transformation-invariant Neural Network
DIVA: Domain Invariant Variational Autoencoders
Fast Inference in Capsule Networks Using Accumulated Routing Coefficients
CapsGAN: Using Dynamic Routing for Generative Adversarial Networks
Deformable Classifiers
Learning Steerable Filters for Rotation Equivariant CNNs
Polar Transformer Networks
Deep Rotation Equivariant Network
Harmonic Networks: Deep Translation and Rotation Equivariance
Local Group Invariant Representations via Orbit Embeddings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.