{"total":67,"rows":[{"id":"2103.01315","title":"Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning","created":"2021-03-01","cited_by_count":56,"area":null},{"id":"2210.02984","title":"The Lie Derivative for Measuring Learned Equivariance","created":"2022-10-06","cited_by_count":54,"area":null},{"id":"2211.11962","title":"Transformation-Equivariant 3D Object Detection for Autonomous Driving","created":"2022-11-22","cited_by_count":52,"area":null},{"id":"2302.14325","title":"BEVPlace: Learning LiDAR-based Place Recognition using Bird's Eye View Images","created":"2023-02-28","cited_by_count":39,"area":null},{"id":"2110.12638","title":"Deep Learning for UAV-based Object Detection and Tracking: A Survey","created":"2021-10-25","cited_by_count":33,"area":null},{"id":"2202.13094","title":"RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning","created":"2022-02-26","cited_by_count":29,"area":null},{"id":"2107.14519","title":"Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions","created":"2021-07-30","cited_by_count":26,"area":null},{"id":"2206.09880","title":"Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities","created":"2022-06-20","cited_by_count":23,"area":null},{"id":"2211.11812","title":"RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network","created":"2022-11-21","cited_by_count":21,"area":null},{"id":"2204.03640","title":"Equivariance Discovery by Learned Parameter-Sharing","created":"2022-04-07","cited_by_count":18,"area":null},{"id":"2210.13150","title":"A PAC-Bayesian Generalization Bound for Equivariant Networks","created":"2022-10-24","cited_by_count":18,"area":null},{"id":"2106.06418","title":"Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales","created":"2021-06-11","cited_by_count":17,"area":null},{"id":"2310.08429","title":"Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks","created":"2023-10-12","cited_by_count":16,"area":null},{"id":"2110.00899","title":"Anti-aliasing Deep Image Classifiers using Novel Depth Adaptive Blurring and Activation Function","created":"2021-10-03","cited_by_count":11,"area":null},{"id":"2106.10044","title":"Training or Architecture? How to Incorporate Invariance in Neural Networks","created":"2021-06-18","cited_by_count":9,"area":null},{"id":"2212.06079","title":"Robust Perception through Equivariance","created":"2022-12-12","cited_by_count":9,"area":null},{"id":"2405.15932","title":"Steerable Transformers for Volumetric Data","created":"2024-05-24","cited_by_count":8,"area":null},{"id":"2111.14507","title":"SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting","created":"2021-11-29","cited_by_count":7,"area":null},{"id":"2202.03967","title":"Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration","created":"2022-02-08","cited_by_count":7,"area":null},{"id":"2104.11244","title":"Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering Transforms","created":"2021-04-22","cited_by_count":6,"area":null},{"id":"2106.08038","title":"Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations","created":"2021-06-15","cited_by_count":6,"area":null},{"id":"2111.03420","title":"Sampling Equivariant Self-attention Networks for Object Detection in Aerial Images","created":"2021-11-05","cited_by_count":6,"area":null},{"id":"2103.07640","title":"Generating Unrestricted Adversarial Examples via Three Parameters","created":"2021-03-13","cited_by_count":5,"area":null},{"id":"2111.14157","title":"Implicit Equivariance in Convolutional Networks","created":"2021-11-28","cited_by_count":5,"area":null},{"id":"2302.11349","title":"Steerable Equivariant Representation Learning","created":"2023-02-22","cited_by_count":5,"area":null},{"id":"2505.19799","title":"A Regularization-Guided Equivariant Approach for Image Restoration","created":"2025-05-26","cited_by_count":5,"area":null},{"id":"2508.05160","title":"Rotation Equivariant Arbitrary-scale Image Super-Resolution","created":"2025-08-07","cited_by_count":5,"area":null},{"id":"2502.13660","title":"Towards Invariance to Node Identifiers in Graph Neural Networks","created":"2025-02-19","cited_by_count":4,"area":null},{"id":"2503.06369","title":"Spectral State Space Model for Rotation-Invariant Visual Representation Learning","created":"2025-03-09","cited_by_count":4,"area":null},{"id":"2101.03603","title":"Target Detection and Segmentation in Circular-Scan Synthetic-Aperture-Sonar Images using Semi-Supervised Convolutional Encoder-Decoders","created":"2021-01-10","cited_by_count":3,"area":null},{"id":"2104.14124","title":"Condensation-Net: Memory-Efficient Network Architecture with Cross-Channel Pooling Layers and Virtual Feature Maps","created":"2021-04-29","cited_by_count":3,"area":null},{"id":"2108.13993","title":"Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods","created":"2021-08-31","cited_by_count":3,"area":null},{"id":"2109.06861","title":"Nonlinearities in Steerable SO(2)-Equivariant CNNs","created":"2021-09-14","cited_by_count":3,"area":null},{"id":"2204.02958","title":"LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity","created":"2022-04-06","cited_by_count":3,"area":null},{"id":"2309.01081","title":"Orientation-Independent Chinese Text Recognition in Scene Images","created":"2023-09-03","cited_by_count":3,"area":null},{"id":"2410.10417","title":"A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning","created":"2024-10-14","cited_by_count":3,"area":null},{"id":"2509.11218","title":"Geometrically Constrained and Token-Based Probabilistic Spatial Transformers","created":"2025-09-14","cited_by_count":3,"area":null},{"id":"2602.08047","title":"Vanilla Group Equivariant Vision Transformer: Simple and Effective","created":"2026-02-08","cited_by_count":3,"area":null},{"id":"2101.07832","title":"The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions","created":"2021-01-19","cited_by_count":2,"area":null},{"id":"2106.09996","title":"Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network","created":"2021-06-18","cited_by_count":2,"area":null},{"id":"2203.05006","title":"Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization","created":"2022-03-09","cited_by_count":2,"area":null},{"id":"2209.11916","title":"A Simple Strategy to Provable Invariance via Orbit Mapping","created":"2022-09-24","cited_by_count":2,"area":null},{"id":"2306.16058","title":"DUET: 2D Structured and Approximately Equivariant Representations","created":"2023-06-28","cited_by_count":2,"area":null},{"id":"2311.08525","title":"Efficient Rotation Invariance in Deep Neural Networks through Artificial Mental Rotation","created":"2023-11-14","cited_by_count":2,"area":null},{"id":"2411.02271","title":"On the Utilization of Unique Node Identifiers in Graph Neural Networks","created":"2024-11-04","cited_by_count":2,"area":null},{"id":"2505.13281","title":"Computer Vision Models Show Human-Like Sensitivity to Geometric and Topological Concepts","created":"2025-05-19","cited_by_count":2,"area":"mllm"},{"id":"2105.10175","title":"Rotation invariant CNN using scattering transform for image classification","created":"2021-05-21","cited_by_count":1,"area":null},{"id":"2105.11636","title":"FILTRA: Rethinking Steerable CNN by Filter Transform","created":"2021-05-25","cited_by_count":1,"area":null},{"id":"2106.04009","title":"Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions","created":"2021-06-07","cited_by_count":1,"area":null},{"id":"2202.12509","title":"RRL:Regional Rotation Layer in Convolutional Neural Networks","created":"2022-02-25","cited_by_count":1,"area":null},{"id":"2305.19207","title":"Group Invariant Global Pooling","created":"2023-05-30","cited_by_count":1,"area":null},{"id":"2404.11309","title":"Achieving Rotation-Invariant Convolution via Non-Learnable Orientation Alignment Operators","created":"2024-04-17","cited_by_count":1,"area":null},{"id":"2405.13850","title":"Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks","created":"2024-05-22","cited_by_count":1,"area":null},{"id":"2411.01624","title":"PreCM: The Padding-based Rotation Equivariant Convolution Mode for Semantic Segmentation","created":"2024-11-03","cited_by_count":1,"area":null},{"id":"2603.09138","title":"Rotation Equivariant Mamba for Vision Tasks","created":"2026-03-10","cited_by_count":1,"area":"ssm"},{"id":"2605.06903","title":"MELD: Multi-Task Equilibrated Learning Detector for AI-Generated Text","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2107.10524","title":"Geometric Data Augmentation Based on Feature Map Ensemble","created":"2021-07-22","cited_by_count":0,"area":null},{"id":"2111.09779","title":"Wiggling Weights to Improve the Robustness of Classifiers","created":"2021-11-18","cited_by_count":0,"area":null},{"id":"2201.05781","title":"OneDConv: Generalized Convolution For Transform-Invariant Representation","created":"2022-01-15","cited_by_count":0,"area":null},{"id":"2208.00627","title":"A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval","created":"2022-08-01","cited_by_count":0,"area":null},{"id":"2305.13536","title":"Subspace-Configurable Networks","created":"2023-05-22","cited_by_count":0,"area":null},{"id":"2305.14462","title":"Sorted Convolutional Network for Achieving Continuous Rotational Invariance","created":"2023-05-23","cited_by_count":0,"area":null},{"id":"2601.05604","title":"Learning Geometric Invariance for Gait Recognition","created":"2026-01-09","cited_by_count":0,"area":null},{"id":"2602.20930","title":"Computing a Characteristic Orientation for Rotation-Independent Image Analysis","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2606.24178","title":"Zero-Shot Test-Time Canonicalization using Out-of-Distribution Scoring","created":"2026-06-23","cited_by_count":0,"area":null},{"id":"2609.36237","title":"Transversal Pooling Neural Networks","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.17424","title":"On Making $\\mathrm{SE}(2)$-Invariant Networks Optimal","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}