0
0

Delete article

Deleted articles cannot be recovered.

Draft of this article would be also deleted.

Are you sure you want to delete this article?

More than 3 years have passed since last update.

Transformer系、CNN系の主要?論文の引用数遷移(ViT,SAN,Transformer,DETR,BiT,,,YOLO,,,VTAB,,XAI)

Last updated at Posted at 2021-02-20

Transformer系、CNN系の主要?論文の引用数遷移

Google Scholarで確認しました。

|分類/番号|略称|名称|日付毎の引用回数|||||||
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
||||2021/2/20|2/28||||||
|Transformer系|||||||||||
|(1)|ViT|AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE|48|
|(2)|Visual Transformers|Visual Transformers: Token-basedImage Representation and Processing for Computer Vision|4|
|(3)|SAN|Exploring Self-attention for Image Recognition|30||
|(4)|Transformer|Attention Is All You Need|17,724||
|(5)|Point Transformer|Point Transformer|3||
|CNN-Transformer系|||||||||||
|(6)|DETR|End-to-End Object Detection with Transformers|123||
|CNN系|||||||||||
|(7)|BiT|Large Scale Learning of General Visual Representations for Transfer|41||
|(8)|Efficient-net|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.|1,662|
|(9)|ResNet|Deep Residual Learning or Image Recognition|70,138|
|CNN 個別技術要素系|||||||||||
|(10)|CNN 位置情報|HOW MUCH POSITION INFORMATION DO CONVOLUTIONAL NEURAL NETWORKS ENCODE?|27|
|Segmentation系|||||||||||
|(11)|U-net|Convolutional Networks for Biomedical Image Segmentation|23,248|
|(12)|DeepLab v3+|Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation|3,014|
|その他(YOLO)系|||||||||||
|(13)|YOLO|You Only Look Once:Unified, Real-Time Object Detection|14,175||
|(14)|YOLO v3|YOLOv3: An Incremental Improvement|5,754||
|(15)|YOLO v4|YOLOv4: Optimal Speed and Accuracy of Object Detection|422||
|その他系|||||||||||
|(16)|GPT-3|Language Models are Few-Shot Learners|566||
|データセット系|||||||||||
|(17)|ImageNet(Done)|Are we done with ImageNet?|7||
|(18)|JFT-300M|Revisiting Unreasonable Effectiveness of Data in Deep Learning Era|735||
|(19)|VTAB|The Visual Task Adaptation Benchmark|20||
|説明(性)系|||||||||||
|(20)|LIME|" Why should i trust you?" Explaining the predictions of any classifier||4,802|
|(21)|Grad-CAM|Grad-cam: Visual explanations from deep networks via gradient-based localization||3,912|
|(22)|XAI|A survey of the state of explainable AI for natural language processing||4|

2021/2/20調査結果に関するコメント

初回なので、よくわからない。論文が出された日付は書いたほうがいい気が。。。

補足

(どうでもいい内容なんですが、、、)データの記載間違いがあると嫌なので、一応、魚拓を保存しています。


(入力確認)チェックリスト

  •  2021/02/20
  •  2021/02/20(同日、追加)

最初は、細かい頻度で見るかも。飽きたら、半年に1回とかに。

0
0
0

Register as a new user and use Qiita more conveniently

  1. You get articles that match your needs
  2. You can efficiently read back useful information
  3. You can use dark theme
What you can do with signing up
0
0

Delete article

Deleted articles cannot be recovered.

Draft of this article would be also deleted.

Are you sure you want to delete this article?