https://github.com/x-plug/mplug
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
Papers with Code 아카이브
Computational Efficiency
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Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xahidbuffon/SUIM
Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
https://github.com/xi-j/mamba-asr
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/xiangyu-liu/DBSR
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
https://github.com/xinjie-q/ldmic
Checkerboard Context Model for Efficient Learned Image Compression
https://github.com/xiuqhou/relation-detr
Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
https://github.com/xue-pai/FuxiCTR
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
https://github.com/xunull/read-Salience-DETR
Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
https://github.com/yangalan123/anhp-andtt
https://github.com/yangyucheng000/University/tree/main/model-1/rwkv
RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks
https://github.com/yangyucheng000/University/tree/main/model-3/rwkv
https://github.com/yashs662/LMBiS-Net
https://github.com/ycjungSubhuman/process-arxiv-pdf
MMD GAN: Towards Deeper Understanding of Moment Matching Network
https://github.com/yhpeter/awesome-rag-evaluation
https://github.com/yikangshen/megablocks
Mixture of Attention Heads: Selecting Attention Heads Per Token
https://github.com/yingkaisha/keras-unet-collection
https://github.com/yokoxue/LpDL
https://github.com/youngandbin/pfotgnrec
Mean-Variance Efficient Collaborative Filtering for Stock Recommendation
https://github.com/yuhengsss/msvmamba
https://github.com/yykzjh/pmfsnet
https://github.com/z-bingo/kernel-prediction-networks-PyTorch
Multi-Kernel Prediction Networks for Denoising of Burst Images
https://github.com/zaeemzadeh/Active-Learning-UCF101-IPM
https://github.com/zaeemzadeh/IPM
https://github.com/zh320/realtime-semantic-segmentation-pytorch
LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
https://github.com/zhangyezhuo/mamca
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/zhengchen1999/cat
Spatial-Spectral Transformer for Hyperspectral Image Denoising
https://github.com/zhengjingwei/cluster_GCN
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
https://github.com/zhengyang-wang/3D-Unet--Tensorflow
U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation
https://github.com/zhn6818/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/zhu-0108/mts-unmixers
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/zivaharoni/capacity-rl
Computing the Feedback Capacity of Finite State Channels using Reinforcement Learning
https://github.com/ziyouzheilu/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/zjZSTU/GoogLeNet
https://github.com/zjuym/chinese_cws_ner
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
https://github.com/zoj613/polya-gamma
Bayesian inference for logistic models using Polya-Gamma latent variables
https://github.com/zs1314/microscopic-mamba
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.