Papers with Code 아카이브

2D Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

27

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/MindSpore-scientific-2/code-5/tree/main/fisheradversarial-mindspore

Robust and Accurate Object Detection via Adversarial Learning

https://github.com/MindSpore-scientific-2/code-7/blob/main/ms_spike_rev_reuse.py

SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams

https://github.com/MindSpore-scientific-2/code-8/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific-2/code-8/tree/main/SING

Single-Shot Refinement Neural Network for Object Detection

https://github.com/MindSpore-scientific-2/code-8/tree/main/STCrowd

STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes

https://github.com/MindSpore-scientific-2/code-8/tree/main/Super-Resolution-for-Root-Imaging

Super Resolution for Root Imaging

https://github.com/MindSpore-scientific/code-1/tree/main/MicAugment

MicAugment: One-shot Microphone Style Transfer

https://github.com/MindSpore-scientific/code-1/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-10/tree/main/MicAugment

Dilated Neighborhood Attention Transformer

https://github.com/MindSpore-scientific/code-10/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-10/tree/main/SING

Single-Shot Refinement Neural Network for Object Detection

https://github.com/MindSpore-scientific/code-10/tree/main/SmeLU-master

Smell and Emotion: Recognising emotions in smell-related artworks

https://github.com/MindSpore-scientific/code-11/tree/main/MicAugment

MicAugment: One-shot Microphone Style Transfer

https://github.com/MindSpore-scientific/code-11/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-11/tree/main/SING

Single-Shot Refinement Neural Network for Object Detection

https://github.com/MindSpore-scientific/code-11/tree/main/Uncertainty_Calibration_Object_Detection

Uncertainty Calibration and its Application to Object Detection

https://github.com/MindSpore-scientific/code-12/tree/main/EPRNet

EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network

https://github.com/MindSpore-scientific/code-13/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-13/tree/main/SING

Single-Shot Refinement Neural Network for Object Detection

https://github.com/MindSpore-scientific/code-13/tree/main/VAE-Creative-Discovery-using-QD-Search

VAE based Text Style Transfer with Pivot Words Enhancement Learning

https://github.com/MindSpore-scientific/code-13/tree/main/pixelshuffle1d

PixelShuffler: A Simple Image Translation Through Pixel Rearrangement

https://github.com/MindSpore-scientific/code-14/tree/main/WaveCRN-master

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

https://github.com/MindSpore-scientific/code-14/tree/main/res_mlp_ms

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/MindSpore-scientific/code-14/tree/main/segmentation-dataset

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/MindSpore-scientific/code-14/tree/main/weakly-supervised

Weakly Supervised Deep Detection Networks

https://github.com/MindSpore-scientific/code-2/tree/main/squeeze-and-attention-networks-for-semantic

Squeeze-and-Excitation Networks

https://github.com/MindSpore-scientific/code-2/tree/main/ssFPN

Feature Pyramid Networks for Object Detection

https://github.com/MindSpore-scientific/code-3/tree/main/corss-dataset-training

Cross-dataset Training for Class Increasing Object Detection

https://github.com/MindSpore-scientific/code-5/tree/main/Projected-Distribution

Projected Distribution Loss for Image Enhancement

https://github.com/MindSpore-scientific/code-5/tree/main/polyloss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-6/tree/main/Neural-Side-by-Side

Neural Extractive Summarization with Side Information

https://github.com/MindSpore-scientific/code-7/tree/main/YOLOS

DETRs Beat YOLOs on Real-time Object Detection

https://github.com/MindSpore-scientific/code-7/tree/main/dino

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/MindSpore-scientific/code-8/tree/main/MicAugment

Dilated Neighborhood Attention Transformer

https://github.com/MindSpore-scientific/code-8/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific/code-8/tree/main/Super-Resolution-for-Root-Imaging

Super Resolution for Root Imaging

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