https://github.com/gmum/ProtoPShare
ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery
Papers with Code 아카이브
Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gmum/Zero-Time-Waste
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
https://github.com/gmum/dl-mo-2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/gnoses/ViT_examples
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/gnouhp/PyTorch-CoordConv
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
https://github.com/godhj93/MobileViT
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
https://github.com/godimarcovr/interpretable_visual_summaries
https://github.com/godwinrayanc/YOLOv1-Pytorch
https://github.com/gofinge/pointtransformerv2
https://github.com/gojkoc54/HOG_SVM_Vehicle_Detection
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
https://github.com/gokulprasadthekkel/pytorch-multi-class-focal-loss
https://github.com/gokyeongryeol/MAHA
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
https://github.com/gomboskriszta/yolo
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/gomerudo/nas-dmrl
BlockQNN: Efficient Block-wise Neural Network Architecture Generation
https://github.com/gomerudo/nas-env
BlockQNN: Efficient Block-wise Neural Network Architecture Generation
https://github.com/gomezzz/distmsmatch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/google-deepmind/jax_privacy
Unlocking High-Accuracy Differentially Private Image Classification through Scale
https://github.com/google-deepmind/kinetics-i3d
https://github.com/google-deepmind/optax
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
https://github.com/google-research/crest
https://github.com/google-research/gestalt
Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure
https://github.com/google-research/google-research/tree/master/capsule_em
https://github.com/google-research/google-research/tree/master/enas_lm
https://github.com/google-research/google-research/tree/master/genomics_ood
https://github.com/google-research/google-research/tree/master/milking_cowmask
https://github.com/google-research/google-research/tree/master/mutual_information_representation_learning
On Mutual Information Maximization for Representation Learning
https://github.com/google-research/maxim
https://github.com/google-research/mood-board-search
https://github.com/google-research/s4l
https://github.com/google-research/si-score
On Robustness and Transferability of Convolutional Neural Networks
https://github.com/google/autol2
On the training dynamics of deep networks with $L_2$ regularization
https://github.com/google/brain-tokyo-workshop/tree/master/WANNRelease
https://github.com/google/flax/tree/master/examples/imagenet
https://github.com/google/prettytensor
https://github.com/gopi-kampli/ImageClusteringDL
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/gorakraj/earlyexit_onnx
Multi-Scale Dense Networks for Resource Efficient Image Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.