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### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no simi…
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- Y-aware Contrastive Loss: perda contrastiva com marge consciente de Y, onde Y é alguma propriedade das amostras e/ou pares.
- Inspirado nas modificações da perda softmax:
- ArcFace: introduz…
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Objective: Identify the most important criteria which will give good returns for robust stocks.
Utilize **machine learning** on stocks on uptrend. Identify the most important breakout conditions to…
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### Is there an existing issue for this?
- [X] I have searched the existing issues
### Feature Description
Credit card fraud is a significant issue that affects both consumers and financial institu…
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## ひとことで表すと
人間の視覚にはshape-biasがあることに着目し、モデルにShape-biasをかける手法を提案。これにより、画像のスタイル変換・ノイズ・AEに対するモデルのロバスト性の向上を目的とした。Robustnessの観点で、Shape-biasに着目した初の論文。
手法としては、新たなDropOutレイヤーを提案した。"InfoDrop"とよぶ。
## 論文リンク
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Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high “standard” accuracy to produce an …
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Thanks for maintaining the list of papers on long-tailed learning!
Our work : **SURE: SUrvey REcipes for building reliable and robust deep networks**[CVPR2024] addressed long-tailed distribution in …
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List in General:
**Can Be Done Without Training Images**
1. Gamma Correction: Adjust image brightness.
2. Filtering: Apply Gaussian blur, median filter, etc.
3. Color Space Transformation: Conve…
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### Feature Summary
I'd like to contribute to FinVeda by implementing a machine learning module that can predict financial trends, stock prices, and customer behavior. This module will leverage popul…
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This project demonstrates a comprehensive testing framework using Cucumber, Playwright, and other tools, covering UI, API, performance, accessibility, and machine learning model testing. Here's a brea…