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2D Classification

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

https://github.com/asahi417/relbert

Language Models are Few-Shot Learners

https://github.com/aschethor/Spline_PINN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/aschethor/Unsupervised_Deep_Learning_of_Incompressible_Fluid_Dynamics

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/aseidelo/wiki_generator

Generating Wikipedia by Summarizing Long Sequences

https://github.com/ash3n/Latent-Similarity

Prototypical Networks for Few-shot Learning

https://github.com/ash3n/Prototypical-Network-TF

Prototypical Networks for Few-shot Learning

https://github.com/asharakeh/kitti_native_evaluation

Joint 3D Proposal Generation and Object Detection from View Aggregation

https://github.com/ashawkey/torch-ngp

Zero-Shot Text-Guided Object Generation with Dream Fields

https://github.com/ashikshafi08/learning_to_see_in_the_dark_Fastai

Learning to See in the Dark

https://github.com/ashishpapanai/uNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras

Deep Residual Learning for Image Recognition

https://github.com/ashishsalunkhe/Understanding-RCNN-Fast-RCNN-Faster-RCNN-and-Mask-RCNN

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/ashley-kim521/11785-project-active-learning-for-YOLO

Learning Loss for Active Learning

https://github.com/ashura1234/soccer-line-detection

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/ashwath007/amenity-detection

SSD: Single Shot MultiBox Detector

https://github.com/ashwath007/aminity-detection

SSD: Single Shot MultiBox Detector

https://github.com/asprenger/keras_fc_densenet

Deep Residual Learning for Image Recognition

https://github.com/asrafulashiq/dynamic-cdfsl

A Broad Study on the Transferability of Visual Representations with Contrastive Learning

https://github.com/asraman9792/Generative-Adversarial-Networks-implementation-of-KERAS

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/asraman9792/Image-Dehazing-GANS

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/assafelovic/gpt-researcher

Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

https://github.com/astaxanthin/adasp

TOOD: Task-aligned One-stage Object Detection

https://github.com/astoycos/Mini_Project2

Deep Residual Learning for Image Recognition

https://github.com/astro-informatics/rcGAN

A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems

https://github.com/asultan123/DL_LLC_Prefetching

LambdaNetworks: Modeling Long-Range Interactions Without Attention

https://github.com/asvath/mobile_robotics

nuScenes: A multimodal dataset for autonomous driving

https://github.com/asylve/Sea-Ice

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/asyml/texar/tree/master/examples/text_style_transfer

Unsupervised Text Style Transfer using Language Models as Discriminators

https://github.com/asyrovprog/cs230project

Mask R-CNN

https://github.com/atharva-18/Neural-Style-Transfer

A Neural Algorithm of Artistic Style

https://github.com/atharva417/Yoga_Pose_GNR638

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/athulshibu/Rewarded-meta-pruning

Revisiting Random Channel Pruning for Neural Network Compression

https://github.com/atregret/mobilenetv3

Searching for MobileNetV3

https://github.com/atulkumarin/DeClare

DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning

https://github.com/au1206/Enhance-GAN

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

https://github.com/aubricot/object_detection_for_image_cropping

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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