Papers with Code 아카이브

Data Compression

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/3rdcore/prequentialcode

공식 코드

In-context learning and Occam's razor

https://github.com/AI4CogComm/CAMC

공식 코드

Edge Learning Based Collaborative Automatic Modulation Classification for Hierarchical Cognitive Radio Networks

https://github.com/AKASH2907/deepfakes_video_classification

공식 코드

Detecting Deepfakes with Metric Learning

https://github.com/BNL-DAQ-LDRD/NeuralCompression

공식 코드

Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder

https://github.com/BNL-DAQ-LDRD/NeuralCompression_v3

공식 코드

Variable Rate Neural Compression for Sparse Detector Data

https://github.com/BerivanIsik/SuRP

공식 코드

An Information-Theoretic Justification for Model Pruning

https://github.com/EmilienDupont/coin

공식 코드

COIN: COmpression with Implicit Neural representations

https://github.com/JiwanSeo/RAQ-VAE

공식 코드

Rate-Adaptive Quantization: A Multi-Rate Codebook Adaptation for Vector Quantization-based Generative Models

https://github.com/Ma-Lab-Berkeley/MCR2

공식 코드

ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction

https://github.com/Ma-Lab-Berkeley/ReduNet

공식 코드

ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction

https://github.com/NJUVISION/MPA

공식 코드

All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation

https://github.com/PatrikOkanovic/RS2

공식 코드

Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning

https://github.com/SIJIEJI/CLNet

공식 코드

CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback

https://github.com/Soptq/Overlap-FedAvg

공식 코드

Communication-Efficient Federated Learning with Compensated Overlap-FedAvg

https://github.com/Wangkkklll/UniPCGC

공식 코드

UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach

https://github.com/Xihaier/INR-TPC-Compression

공식 코드

Efficient Compression of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling

https://github.com/adgaudio/heartspot

공식 코드

HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection

https://github.com/aidetx/aidetx

공식 코드

AIDetx: a compression-based method for identification of machine-learning generated text

https://github.com/aizhanaakhmet/data-compression-inference-in-cosmology-with-ssl

공식 코드

Data Compression and Inference in Cosmology with Self-Supervised Machine Learning

https://github.com/algorithmicdatareduction/pytorch-quadconv

공식 코드

QuadConv: Quadrature-Based Convolutions with Applications to Non-Uniform PDE Data Compression

https://github.com/alipay/l3tc-leveraging-rwkv-for-learned-lossless-low-complexity-text-compression

공식 코드

L3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text Compression

https://github.com/arnaumarin/hdnn-artifactbrainstate

공식 코드

Hopfield-Enhanced Deep Neural Networks for Artifact-Resilient Brain State Decoding

https://github.com/bamler-lab/understanding-ans

공식 코드

Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective

https://github.com/bnl-daq-ldrd/neuralcompression_v2

공식 코드

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

https://github.com/bornabr/rscnet

공식 코드

RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing

https://github.com/cambridge-mlg/recombiner

공식 코드

RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations

https://github.com/comedialkb/learning_with_passive_optical_nonlinear_mapping

공식 코드

Nonlinear optical encoding enabled by recurrent linear scattering

https://github.com/cscribano/dct-former-public

공식 코드

DCT-Former: Efficient Self-Attention with Discrete Cosine Transform

https://github.com/cuMF/cumf_als

공식 코드

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

https://github.com/danideniz/davishanddataset-events

공식 코드

Event-based Vision for Early Prediction of Manipulation Actions

https://github.com/daniel-bogdoll/deep_generative_models

공식 코드

Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models

https://github.com/danjacobellis/mpq

공식 코드

Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Models

https://github.com/diaoenmao/Dimension-Reduced-Turbulent-Flow-Data-From-Deep-Vector-Quantizers

공식 코드

A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow

https://github.com/dmlc/xgboost

공식 코드

XGBoost: A Scalable Tree Boosting System

https://github.com/dpelacani/AWLoss

공식 코드

Convolve and Conquer: Data Comparison with Wiener Filters

https://github.com/elki-project/elki

공식 코드

BETULA: Numerically Stable CF-Trees for BIRCH Clustering

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