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보존된 평가 기록

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코드

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

https://github.com/ayushmankumar7/SegNet---Tensorflow-2

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/ayushpalak/darknet_cokebottles

YOLO9000: Better, Faster, Stronger

https://github.com/ayushpalak/darknet_multi_cokebottles

YOLO9000: Better, Faster, Stronger

https://github.com/azy64/Deep-Learning

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/b-akshay/aknn-classifier

An adaptive nearest neighbor rule for classification

https://github.com/b-carter/overinterpretation

Overinterpretation reveals image classification model pathologies

https://github.com/backSpace001/PMR_WEB_APP1.0

YOLO9000: Better, Faster, Stronger

https://github.com/bagmk/Quantum_Machine_Learning_Express

Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability

https://github.com/bailejor/SAMSHA_Transfer_Learning

SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data

https://github.com/bailvwangzi/repulsion_loss_ssd

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/baiyang2464/chatbot-base-on-Knowledge-Graph

Convolutional Neural Networks for Sentence Classification

https://github.com/baky0905/hurricane-data-engineering

Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks

https://github.com/baldassarreFe/deep-koalarization

CNN Features off-the-shelf: an Astounding Baseline for Recognition

https://github.com/banben/maskrcnn-benchmark

Is Heuristic Sampling Necessary in Training Deep Object Detectors?

https://github.com/baohq1595/aae-experiment

Adversarial Autoencoders

https://github.com/baokuiwang/context_aware_situation_entity

Building Context-aware Clause Representations for Situation Entity Type Classification

https://github.com/barbua/FSA

Feature Selection with Annealing for Computer Vision and Big Data Learning

https://github.com/bareblackfoot/faster-rcnn.selection

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/bareblackfoot/lddp-tf-faster-rcnn

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/barnrang/Prototypical-network-keras-reimplementation

Prototypical Networks for Few-shot Learning

https://github.com/barrykidney/CarND-Behavioral-Cloning-P3

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

https://github.com/bartoszpanek1/Real-time-emotion-recognizer

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/basaltzhang/maskrcnn-benchmark

Is Heuristic Sampling Necessary in Training Deep Object Detectors?

https://github.com/basiralab/RG-Select

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/basiralab/Reproducible-Generative-Learning

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/basiralab/reproduciblefedgnn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/bastiennNB/Pair_ReID

In Defense of the Triplet Loss for Person Re-Identification

https://github.com/baucheng/caffeFA

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

https://github.com/bayraktarbaris/SNGAN

The Devil is in the Decoder: Classification, Regression and GANs

https://github.com/bbbbbbzhou/Weakly-Supervised-ChestXray-AdaptiveDenseNet

A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities

https://github.com/bbbdylan/unisiam

Meta-Learning for Semi-Supervised Few-Shot Classification

https://github.com/bcaitech1/p4-dkt-no_caffeine_no_gain

How to Fine-Tune BERT for Text Classification?

https://github.com/bcmi/CaGNetv2-Zero-Shot-Semantic-Segmentation

Zero-Shot Semantic Segmentation

https://github.com/bcsrn/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/bdqnghi/bi-tbcnn

Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks

https://github.com/beandkay/epass

USB: A Unified Semi-supervised Learning Benchmark for Classification

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