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/opent03/LangevinDQN-torch
https://github.com/openvinotoolkit/anomalib
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/osmr/imgclsmob
https://github.com/p0p4k/vits2_pytorch
https://github.com/panopticon-fm/panopticon
SpectralEarth: Training Hyperspectral Foundation Models at Scale
https://github.com/paperswithcode/model-index
https://github.com/patiencefromzhou/simplehand
https://github.com/pavliclab/neurips2023-unireps-hong-tgsh-randomly_weighted_neuromodulation_in_neural_networks
https://github.com/pbaylies/stylegan-encoder
https://github.com/pfmarteau/HIF
Hybrid Isolation Forest - Application to Intrusion Detection
https://github.com/phymhan/soda-diffusion
Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models
https://github.com/piskovskyi/fMRI-VisionMamba
https://github.com/pkuyym/evolvingattention
A Transformer-based Framework for Multivariate Time Series Representation Learning
https://github.com/pltrees/abcboost
Fast ABC-Boost: A Unified Framework for Selecting the Base Class in Multi-Class Classification
https://github.com/pminhtam/KPN_attention
Multi-Kernel Prediction Networks for Denoising of Burst Images
https://github.com/pminhtam/MWCNN
https://github.com/pmiraldo/MinimalMultiPerspectivePose
A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines
https://github.com/prajjwal1/fluence
Adaptive Transformers for Learning Multimodal Representations
https://github.com/premthomas/keras-image-classification
https://github.com/puxinhe/im2txt_v3
https://github.com/pwc-1/Paper-9/tree/main/1/vit_mae
https://github.com/pwc-1/Paper-9/tree/main/4/TSA_mindspore
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
https://github.com/pwc-1/Paper-9/tree/main/7/Simba
SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
https://github.com/pytorch/tensordict
https://github.com/pytorch/vision
https://github.com/qdata/fasjem
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
https://github.com/qihongl/demo-random-search-RL
Simple random search provides a competitive approach to reinforcement learning
https://github.com/qingyu95/ICDAR2017-DATASET
https://github.com/qitianwu/nodeformer
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
https://github.com/qizhipei/fabind
https://github.com/qslim/PDF
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
https://github.com/r-karthik/Detection-of-pests
https://github.com/rai-project/dlframework
https://github.com/raj-gupta1/Tomato-leaves-disease-Predictor
https://github.com/raja17021998/Augmented-Random-Search-Implementation
Simple random search provides a competitive approach to reinforcement learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.