Tag：loss
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[paper] [semi supervised semantic segmentation]advantageous learning for semi supervised semantic segmentation
Adversarial Learning for Semi-Supervised Semantic Segmentation Original paper abstract Innovation: we propose a semi supervised semantic segmentation method using confrontation network.In the traditional Gan network, the discriminator is mostly used to classify the authenticity of the input image (the image of the sample in datasets is scored high, and the image generated by the generator […]
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What is the difference between the metrics of the model and the loss function? Why are both important in a project?
Have you been using your loss function to evaluate the performance of your machine learning system? I believe many people do the same, which is a common misunderstanding, because the program default settings in AI and the introduction in the course all say so. In this article, I will explain why two independent model scoring […]
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Intuitive and popular explanation from entropy to cross entropy loss
For beginners of machine learning and data science, they must be clear about the concepts of entropy and cross entropy. They are the key basis for building trees, dimensionality reduction and image classification. In this article, I will try to explain the concept of entropy from the perspective of information theory. When I first tried […]
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PSS: you are only two convolution layers away from the NMS free+ point | 2021 paper
This paper proposes a simple and efficient PSS branch, which can remove NMS post-processing only by adding two convolution layers on the basis of the original network, and can also improve the accuracy of the model. The stop grad training method is also very interesting and worth seeing Source: Xiaofei’s algorithm Engineering Notes official account […]
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[statistical learning methods ｜ notes] Chapter 1 Statistical Learning Method Theory
catalogue 1 Statistical Learning 2 supervised learning 2.1 basic concepts 2.2 formalization of problems 3. Three elements of statistical learning 3.1 model 3.2 strategy 3.3 algorithm 4 model evaluation and model selection 4.1 training error and test error 4.2 over fitting and model selection Regularization and cross validation — two commonly used model selection methods […]
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A colloquial machine learning concept
preface:At the request of the publishing house, we plan to produce a popular series of articles on machine learning and in-depth learning. Please give us more suggestions on the shortcomings. 4.1 introduction to machine learning Machine learning seems to be a profound term. In fact, it is in life. The old saying goes: “autumn is […]
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[experience sharing] mindspore model development &modelarts; Multi card training experience sharing
Reprint address:https://bbs.huaweicloud.com/f… Author: Chen Baba The training part of developing a model is roughly divided into data processing, network, loss function and training. Since we mainly implement the replication from pytorch to mindspore, the same parts of the two will not be introduced in detail. 1. data processing: Data sets commonly used in the image […]
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Sparse-MLP A Fully-MLP Architecture with Conditional Computation
Spare MLP a full MLP architecture with conditional computing Original document:https://www.yuque.com/lart/pa… Read the article from the abstract Mixture-of-Experts (MoE) with sparse conditional computation has been proved an effective architecture for scaling attention-based models to more parameters with comparable computation cost. In this paper, we propose Sparse-MLP, scaling the recent MLP-Mixer model with sparse MoE layers, […]
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Knowledge distillation of Bert model: Study on the theory and mechanism of distilbert method
If you have ever trained a large NLP model like Bert or Roberta, you will know that the process is extremely long. Due to its large scale, training such models may last for several days. When you need to run them on small devices, you will find that you are paying for increasing performance at […]
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Training graph convolution network GCN on Cora dataset using pytorch geometry
Graph structure can be seen everywhere in the real world. Roads, social networks and molecular structures can be represented by graphs. Graph is one of the most important data structures we have. There are many resources today that can teach us everything we need to apply machine learning to such data.There have been many theories […]
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Code level profiling tool for performance tools
Last time someone mentioned the analysis tool. So let’s talk about code level profiling tools.No matter how you blow it, the performance loss of code level profiling tools exists.And the loss is not small. Even if it is done at the bottom layer, there is still a lot of loss. 20-30% loss is normal. To […]
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What if the iPhone is locked by a stranger’s Apple ID?
Recently, I received an email from a user. The content of the email showed that the user wanted to download a paid app for free and bought other people’s Apple ID information from a treasure. After logging in, you can download it for free, but it suddenly shows that the iPhone has entered the lost […]