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하위 태스크

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평가표

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보존된 평가 기록

19

코드

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

https://github.com/pwc-1/Paper-9/tree/main/4/PR_Product

PR Product: A Substitute for Inner Product in Neural Networks

https://github.com/pwc-1/Paper-9/tree/main/4/cyclic-cosine-decay-master

Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification

https://github.com/pwc-1/Paper-9/tree/main/7/PR_Product

PR Product: A Substitute for Inner Product in Neural Networks

https://github.com/pwc-1/Paper-9/tree/main/7/Translation-Invariant/model

On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location

https://github.com/pxaris/ccml

AST: Audio Spectrogram Transformer

https://github.com/pyRiemann/pyRiemann

A Plug&Play P300 BCI Using Information Geometry

https://github.com/pygod-team/pygod

One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks

https://github.com/pykeen/ilpc2022

Composition-based Multi-Relational Graph Convolutional Networks

https://github.com/pyro-ppl/pyro

Doubly Stochastic Variational Inference for Deep Gaussian Processes

https://github.com/pytorch/captum

Visualizing and Understanding Convolutional Networks

https://github.com/pytorch/fairseq/blob/master/fairseq/models/wav2vec.py

wav2vec: Unsupervised Pre-training for Speech Recognition

https://github.com/pytorch/vision

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/qilei123/MLKP4OpenImage

Multi-scale Location-aware Kernel Representation for Object Detection

https://github.com/qinglew/FoldingNet

FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation

https://github.com/qingyuanchen1997/Bag-of-Tricks

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/qinhanmin2014/fine-tune-bert-for-text-classification

How to Fine-Tune BERT for Text Classification?

https://github.com/qiuliwang/lidc-idri-toolbox-python

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

https://github.com/qncsn2016/DeepGWC

A Deep Graph Wavelet Convolutional Neural Network for Semi-supervised Node Classification

https://github.com/qq456cvb/AAE

Adversarial Autoencoders

https://github.com/qq456cvb/KdNet-Tensorflow

Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models

https://github.com/qq456cvb/Point-Transformers

Point Transformer

https://github.com/qrfaction/keras-sparsemax

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

https://github.com/qss878448059/ecg_mit

ECG arrhythmia classification using a 2-D convolutional neural network

https://github.com/quanghona/SOLO_tf2

SOLO: Segmenting Objects by Locations

https://github.com/quangvy2703/ABCNet-ESRGAN-SRTEXT

SOLO: Segmenting Objects by Locations

https://github.com/quva-lab/escnn

Steerable CNNs

https://github.com/qwer10/CarDetection

YOLO9000: Better, Faster, Stronger

https://github.com/qxtaiba/Emotional-Dystopia

Classifying and Visualizing Emotions with Emotional DAN

https://github.com/r-khanna/Visualizing-and-Understanding-Convolutional-Networks

Visualizing and Understanding Convolutional Networks

https://github.com/r06942054/NOTE-RCNN

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/ra1nty/sru-naive

Simple Recurrent Units for Highly Parallelizable Recurrence

https://github.com/rabihchamas/dcls-gradcam-eval

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/rachit-shah/News-Classfication-using-DNN-models

Convolutional Neural Networks for Sentence Classification

https://github.com/radar-lab/traffic_monitoring

MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring

https://github.com/radoslawkrolikowski/sentiment-analysis-pytorch

Convolutional Neural Networks for Sentence Classification

https://github.com/radpet/fake-news-detector

Simple Open Stance Classification for Rumour Analysis

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