https://github.com/prajjwal1/autonomous-object-detection
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
Papers with Code 아카이브
Domain Adaptation
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Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/prakhargoyal106/MelanomaClassification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/pranoyr/dynamic-Pix2Pix
https://github.com/preetam1997/Self-Driven-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/prinshul/WBC-Classification-UDA
Target-Independent Domain Adaptation for WBC Classification using Generative Latent Search
https://github.com/priyavrat-misra/xrays-and-gradcam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/prlz77/ResNeXt.pytorch
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/protonx-engineering/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/protonx-tf-04-projects/ConvNext-2020s
https://github.com/pshashk/seesaw-facenet
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/psr007ps/NeuralNetAugmentations
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/pswietojanski/ojsp_adaptation_review_2020
Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview
https://github.com/pszemraj/BoulderAreaDetector
https://github.com/pth051001/ResNeXt50-Implementation-and-Transfer-Learning-with-ResNet50V2
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/purbayankar/Hyperspectral-Vision-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/purdue-m2/covid-19_detection_m2_purdue
https://github.com/pwc-1/Paper-10/tree/main/superpoint
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/pwc-1/Paper-5/tree/main/minicpm
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
https://github.com/pwc-1/Paper-8/tree/main/dinov2
https://github.com/pwc-1/Paper-9/tree/main/2/minicpm
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
https://github.com/pwc-1/Paper-9/tree/main/5/vit_hybrid
Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation
https://github.com/pwc-1/Paper-9/tree/main/6/AVA_hpa/src/RandAugment
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/pwc-1/Paper-9/tree/main/6/Alexnet-ABeffect/test50_feature
https://github.com/py-why/econml
https://github.com/pytorch/vision
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/qcri/dialectal_arabic_segmenter
https://github.com/qcri/dialectal_arabic_tools
https://github.com/qiaopTDUN/mae-repo
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/qiaozhijian/vLPD-Net
A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition
https://github.com/qingyuanchen1997/Bag-of-Tricks
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/qliu24/render-3d-segmentation
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
https://github.com/quanmario0311/ViT_PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/qubvel/efficientnet
https://github.com/qubvel/resnet_152
https://github.com/ra1ph2/Vision-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.