Giter Site home page Giter Site logo

Comments (3)

ZY123-GOOD avatar ZY123-GOOD commented on May 23, 2024

You've raised a good question. Batch Normalization (BN) plays a supportive role here by aiming to estimate the mean and variance of the feature distribution. In practice, statistical methods can also be employed to obtain the mean and variance of features. The selection of hyperparameters is indeed quite subtle, as I've observed that optimal hyperparameters vary across different models. I would like to share with you another piece of our work titled "Rethinking Out-of-Distribution Detection From a Human-Centric Perspective" (https://arxiv.org/abs/2211.16778). This work indicates that the algorithm's performance is significantly influenced by the model structure and parameters. This observation might explain why optimal hyperparameters differ across various models. It also reveals the current difficulty in constructing a cross-model universal detection algorithm. I hope my response proves helpful.
If you have any further questions, please feel free to email me at [email protected] or chat with me on WeChat.

from easyrobust.

xumingyu2021 avatar xumingyu2021 commented on May 23, 2024

Thanks for your reply! It helps me a lot.
Indeed, using statistical methods to obtain the mean and variance of features can be more practical. I think perhaps using quantiles instead of mean/variance might be also a good choice. Of course, there might be more suitable statistics.
I quite agree with your viewpoint in "Rethinking Out-of-Distribution Detection From a Human-Centric Perspective" that "model architectures and training regimes matter in OOD detection and should be considered integral when designing new detection methods." Perhaps future OOD detection methods require insensitive hyperparameters (if any), or can reveal the relationship between hyperparameters and network architectures/model training.

from easyrobust.

ZY123-GOOD avatar ZY123-GOOD commented on May 23, 2024

I agree with you. After realizing the significant impact of the model’s parameters on detection algorithms, I have been recently reflecting on whether there are algorithms that are insensitive to hyperparameters or even delving into how much contribution post-hoc detection methods make to security. Feel free to contact me for further discussion.

from easyrobust.

Related Issues (19)

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.