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2D Classification

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코드

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

https://github.com/aliutkus/speechmetrics

MOSNet: Deep Learning based Objective Assessment for Voice Conversion

https://github.com/allanzelener/YAD2K

YOLO9000: Better, Faster, Stronger

https://github.com/allen108108/Model-Optimizer_Implementation

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/allenai/dnw

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/allenai/macaw

Language Models are Few-Shot Learners

https://github.com/allisontam/bills867

BillSum: A Corpus for Automatic Summarization of US Legislation

https://github.com/alpemek/ais3d

SSD: Single Shot MultiBox Detector

https://github.com/alrhub/arturo

Container: Context Aggregation Network

https://github.com/alrojo/lasagne_residual_network

Deep Residual Learning for Image Recognition

https://github.com/altomator/Introduction_to_Deep_Learning-2-Face_Detection

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/alvarobartt/serving-pytorch-models

Deep Residual Learning for Image Recognition

https://github.com/alvinwan/shiftresnet-cifar

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/alzmzyy/pointtransformer

Point Transformer

https://github.com/aman-garg0001/fruit-classifier

Deep Residual Learning for Image Recognition

https://github.com/amanteur/BandSplitRNN-Pytorch

Music Source Separation with Band-split RNN

https://github.com/amanteur/SCNet-PyTorch

SCNet: Sparse Compression Network for Music Source Separation

https://github.com/amazingcodeLYL/Traffic_signs_detection_darket

YOLOv3: An Incremental Improvement

https://github.com/amazingyyc/Brouhaha

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/amazon-research/dse

Prototypical Networks for Few-shot Learning

https://github.com/amazon-research/long-short-term-transformer

Deep Residual Learning for Image Recognition

https://github.com/amazon-research/mix-generation

Align before Fuse: Vision and Language Representation Learning with Momentum Distillation

https://github.com/amazon-research/stochastic-backpropagation

An In-depth Study of Stochastic Backpropagation

https://github.com/amazon-science/bigdetection

A Simple Semi-Supervised Learning Framework for Object Detection

https://github.com/amazon-science/long-short-term-transformer

Deep Residual Learning for Image Recognition

https://github.com/amazon-science/patchcore-inspection

Towards Total Recall in Industrial Anomaly Detection

https://github.com/amdegroot/ssd.pytorch

SSD: Single Shot MultiBox Detector

https://github.com/amemetov/ssd

SSD: Single Shot MultiBox Detector

https://github.com/amerch/CIFAR100-Training

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/aminekha/AI-For-2022

YOLO9000: Better, Faster, Stronger

https://github.com/amingwu/single-dgod

https://github.com/amirassov/kaggle-imaterialist

Hybrid Task Cascade for Instance Segmentation

https://github.com/amirbar/FilterResponseNormalization

Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

https://github.com/amirdy/dog-breed-classification

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/amirfaraji/LowDoseCTPytorch

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/amirgholami/pyhessian

HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks

https://github.com/amirmohammadii/SSD-object-detection

SSD: Single Shot MultiBox Detector

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