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General Classification

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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/ftramer/Handcrafted-DP

Deep Roto-Translation Scattering for Object Classification

https://github.com/fulviomascara/pytorch-cv

Facial Expression Recognition Research Based on Deep Learning

https://github.com/fursovia/tcav_nlp

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

https://github.com/fxia22/kdnet.pytorch

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

https://github.com/fxs130430/SHAP_FOLD

Induction of Non-Monotonic Rules From Statistical Learning Models Using High-Utility Itemset Mining

https://github.com/g-u-n/pycil

Overcoming catastrophic forgetting in neural networks

https://github.com/g4vrel/AAE

Adversarial Autoencoders

https://github.com/g8a9/ferret

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

https://github.com/gabiguetta/ROCKET

ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

https://github.com/gaborvecsei/Barlow-Twins

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/gabriel-sgama/semantic-superpoint

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/gabrieldernbach/barlow-twins

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/gabrieletiboni/Domain-adaptation-on-PACS-dataset

Domain-Adversarial Training of Neural Networks

https://github.com/gabrielhuang/centroid-networks

Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time

https://github.com/gaelmoccand/RoadSegmentation_CNN

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

https://github.com/galkampel/HyperNetworks

Molecule Property Prediction and Classification with Graph Hypernetworks

https://github.com/gamilton211/Object-detection-YOLO

YOLO9000: Better, Faster, Stronger

https://github.com/gan3sh500/attention-augmented-conv

Attention Augmented Convolutional Networks

https://github.com/gan3sh500/custom-pooling

CBAM: Convolutional Block Attention Module

https://github.com/gan3sh500/local-relational-nets

Local Relation Networks for Image Recognition

https://github.com/ganeshjawahar/interpret_bert

SentEval: An Evaluation Toolkit for Universal Sentence Representations

https://github.com/gaochang-bjtu/1-bit-fqt

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

https://github.com/gaohuang/MSDNet

Multi-Scale Dense Networks for Resource Efficient Image Classification

https://github.com/gaotianyu1350/SentEval

SentEval: An Evaluation Toolkit for Universal Sentence Representations

https://github.com/gary-kaitung/data-science-portfolio

YOLO9000: Better, Faster, Stronger

https://github.com/gatech-eic/vitcod

LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference

https://github.com/gatheluck/pytorch-selectivenet

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

https://github.com/gau-nernst/CenterNet

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

https://github.com/gaussic/text-classification-cnn-rnn

Convolutional Neural Networks for Sentence Classification

https://github.com/gbrltv/business_process_encoding

Characterizing classification datasets: a study of meta-features for meta-learning

https://github.com/gcucurull/maml_flax

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

https://github.com/gcunhase/embracebert

EmbraceNet: A robust deep learning architecture for multimodal classification

https://github.com/gdialektakis/Statistical-Dialogue-Systems-with-Adversarial-AutoEncoders

Adversarial Autoencoders

https://github.com/gear/graph-homomorphism-network

Graph Homomorphism Convolution

https://github.com/gebob19/REPTILE-Metalearning

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

https://github.com/gebob19/cscd94-metalearning

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

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