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Methods/ResNeXt

ResNeXt

Computer VisionIntroduced 2000132 papers

Description

A ResNeXt repeats a building block that aggregates a set of transformations with the same topology. Compared to a ResNet, it exposes a new dimension, cardinality (the size of the set of transformations) $C$ , as an essential factor in addition to the dimensions of depth and width.

Formally, a set of aggregated transformations can be represented as: $\mathcal{F}(x)=\sum_{i=1}^{C}\mathcal{T}_i(x)$ , where $\mathcal{T}_i(x)$ can be an arbitrary function. Analogous to a simple neuron, $\mathcal{T}_i$ should project $x$ into an (optionally low-dimensional) embedding and then transform it.

Papers Using This Method

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention2025-06-18 Segmenting Bi-Atrial Structures Using ResNext Based Framework2025-02-28 Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt2024-12-23 Pairwise Discernment of AffectNet Expressions with ArcFace2024-12-01 Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning2024-09-19 RoBERTa, ResNeXt and BiLSTM with self-attention: The ultimate trio for customer sentiment analysis2024-07-25 PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction2024-01-07 ProtoNER: Few shot Incremental Learning for Named Entity Recognition using Prototypical Networks2023-10-03 Predicting mechanical properties of Carbon Nanotube (CNT) images Using Multi-Layer Synthetic Finite Element Model Simulations2023-07-16 Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting2023-03-19 Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection2022-10-18 Delving into the Estimation Shift of Batch Normalization in a Network2022-03-21 Towards Disentangling Information Paths with Coded ResNeXt2022-02-10 Collision Detection: An Improved Deep Learning Approach Using SENet and ResNext2022-01-13 Rethinking Dilated Convolution for Real-time Semantic Segmentation2021-11-18 A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition2021-11-03 SCA Net: Sparse Channel Attention Module for Action Recognition2021-10-15 Stochastic Anderson Mixing for Nonconvex Stochastic Optimization2021-10-04 Efficient Action Recognition Using Confidence Distillation2021-09-05 Group Fisher Pruning for Practical Network Compression2021-08-02