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channel selection

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Dntfreitas/GA_PSO_DEEP_LEARNING

공식 코드

Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG

https://github.com/HAIx-Lab/SpatialExplanations4BCI

공식 코드

Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distance

https://github.com/Ha0Tang/SelectionGAN

공식 코드

Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation

https://github.com/Nuctech-AI/LBS_pruning

공식 코드

Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed

https://github.com/SCUT-AILab/DCP

공식 코드

Discrimination-aware Channel Pruning for Deep Neural Networks

https://github.com/Soumyabrata/rough-sets

공식 코드

Rough Set Based Color Channel Selection

https://github.com/XU-TIANYANG/GFS-DCF

공식 코드

Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking

https://github.com/bojue-wang/ccm-lrr

공식 코드

Filter Pruning For CNN With Enhanced Linear Representation Redundancy

https://github.com/calmevtime/DCTNet

공식 코드

Learning in the Frequency Domain

https://github.com/calmevtime1990/supp

공식 코드

Learning in the Frequency Domain

https://github.com/guoruilu/slimseiz

공식 코드

SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network

https://github.com/irwinherrmann/stochastic-gates

공식 코드

Channel selection using Gumbel Softmax

https://github.com/itsnotacie/sepprune

공식 코드

SepPrune: Structured Pruning for Efficient Deep Speech Separation

https://github.com/jcwang123/fedlc

공식 코드

Personalizing Federated Medical Image Segmentation via Local Calibration

https://github.com/krishnatarun/ssl_dgc

공식 코드

Dynamic Channel Selection in Self-Supervised Learning

https://github.com/llmir/fedicra

공식 코드

Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation

https://github.com/merantix/acosp

공식 코드

Auto-Compressing Subset Pruning for Semantic Image Segmentation

https://github.com/mlgig/channelselectionmtsc

공식 코드

Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification

https://github.com/mlgig/xai4mtsc_eval_actionability

공식 코드

Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification

https://github.com/mlsys-seo/FtGAN

공식 코드

Testing the Channels of Convolutional Neural Networks

https://github.com/msuimpress/hardmax

공식 코드

Learning Binary Color Filter Arrays with Trainable Hard Thresholding

https://github.com/popcornell/MicRank

공식 코드

Learning to Rank Microphones for Distant Speech Recognition

https://github.com/statistical-deep-learning/dcs-attention

공식 코드

Differentiable Channel Selection in Self-Attention For Person Re-Identification

https://github.com/yihui-he/channel-pruning

공식 코드

Channel Pruning for Accelerating Very Deep Neural Networks

https://github.com/zhangzjn/ocr-gan

공식 코드

Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection

https://github.com/zhengshuchen/hcfnet

공식 코드

HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection

https://github.com/PSCLab-ASU/Learning-in-the-Frequency-Domain

Learning in the Frequency Domain

https://github.com/TakShimoda/CP8322-Deep-Learning-in-Computer-Vision-Course-Project

Learning in the Frequency Domain

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.