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General Classification

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평가표

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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/ZhinusMarzi/Sparsity-based-defenses-against-adversarial-attacks

Combating Adversarial Attacks Using Sparse Representations

https://github.com/Zhiwei-Z/PrompLimitTest

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/Zhiwei-Z/SeqPromp

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/Zhiwei-Z/prompzzw

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/ZhouYanzhao/PRM

Weakly Supervised Instance Segmentation using Class Peak Response

https://github.com/ZhuangCY/Coding-NN

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/ZhuangCY/DGCN

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/ZmeiGorynych/transformer_pytorch

A Structured Self-attentive Sentence Embedding

https://github.com/a1600012888/YOPO-You-Only-Propagate-Once

Adversarial Training for Free!

https://github.com/aaaceo890/Attention

Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition

https://github.com/aakashjhawar/traffic-sign-detection

YOLO9000: Better, Faster, Stronger

https://github.com/aamirfaaiz/Synthetic-Voice-Detection

SwishNet: A Fast Convolutional Neural Network for Speech, Music and Noise Classification and Segmentation

https://github.com/aaron-jx-li/r3-protopnet

This Looks Like That: Deep Learning for Interpretable Image Recognition

https://github.com/aaron-wang-de/ab

YOLO9000: Better, Faster, Stronger

https://github.com/abbaasalif/freematch-improved

USB: A Unified Semi-supervised Learning Benchmark for Classification

https://github.com/abcp4/MyDarts

DARTS: Differentiable Architecture Search

https://github.com/abel-leulseged/Quick-Draw

Network In Network

https://github.com/abhigoyal1997/adv_reprogramming

Adversarial Reprogramming of Neural Networks

https://github.com/abhilash1910/Deep-Graph-Learning

Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning

https://github.com/abhilash1910/MiniAttention

Hierarchical Attentional Hybrid Neural Networks for Document Classification

https://github.com/abhilash1910/SpectralEmbeddings

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

https://github.com/abhishek-924/Capsule-Network

Investigating Capsule Networks with Dynamic Routing for Text Classification

https://github.com/abhishekroushan/CheckeredVizCNN_cs431

Improving the Resolution of CNN Feature Maps Efficiently with Multisampling

https://github.com/abhishirk/Aligned_ReId

In Defense of the Triplet Loss for Person Re-Identification

https://github.com/abhsri/Reimplementation-of-Self-supervised-Classifier

Self-Supervised Classification Network

https://github.com/abi-kothapalli/minimumdominatingsets

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/abimur-123/Canvass_codingchallenge

Multi-label Methods for Prediction with Sequential Data

https://github.com/abstatic/ire_project18

Automatic Argumentative-Zoning Using Word2vec

https://github.com/ac-rodriguez/privilegedpooling

Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up

https://github.com/acambray/SemEval2019-OffensiveTweetClassification

Bidirectional Recurrent Models for Offensive Tweet Classification

https://github.com/ace19-dev/image-retrieval-tf

Deep Cosine Metric Learning for Person Re-Identification

https://github.com/ace19-dev/mvcnn-tf

Deep Cosine Metric Learning for Person Re-Identification

https://github.com/aced125/sparsemax

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

https://github.com/acewjh/Video-Swin-Transformer

Video Swin Transformer

https://github.com/achalagarwal/gcn-lpa

Unifying Graph Convolutional Neural Networks and Label Propagation

https://github.com/achernodub/targer

Neural End-to-End Learning for Computational Argumentation Mining

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