self-supervised | Supervised Representation Learning Official | Machine Learning library

by htdt Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | self-supervised Summary

self-supervised is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning applications. self-supervised has no bugs, it has no vulnerabilities and it has low support. However self-supervised build file is not available. You can download it from GitHub.

Whitening for Self-Supervised Representation Learning | Official repository

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self-supervised has a low active ecosystem.

It has 40 star(s) with 6 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of self-supervised is current.

Quality

self-supervised has 0 bugs and 0 code smells.

Security

self-supervised has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

self-supervised code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

self-supervised does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

self-supervised releases are not available. You will need to build from source code and install.

self-supervised has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed self-supervised and discovered the below as its top functions. This is intended to give you an instant insight into self-supervised implemented functionality, and help decide if they suit your requirements.

Evaluate the model
Evaluate the SSGD algorithm
Get data from loader
Evaluate the top k nearest neighbors
Move the target by a given amount
Update the target and head parameters
Evaluate sgd algorithm
Return a DataLoader for training
Creates training image folder
Augment crop
Train data for training
Train the dataset
Train a training dataset
Train the training dataset
Parse command line options
Return the method corresponding to the given name
Load data loader
Return the DS class given a name
Get scheduler
Evaluate kn
Return data loader

Get all kandi verified functions for this library.

self-supervised Key Features

No Key Features are available at this moment for self-supervised.

self-supervised Examples and Code Snippets

No Code Snippets are available at this moment for self-supervised.

Community Discussions

Trending Discussions on self-supervised

What are the disadvantages of self-supervised learning in ML?

Iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature

How to invert a PyTorch Embedding?

QUESTION

What are the disadvantages of self-supervised learning in ML?

Asked 2021-Sep-08 at 06:35

Self-supervised learning has been on the rise over the past few years. Compared to other learning methods such as supervised and semi-supervised, it does have an edge since it does not require labeled data.

I would like to know if self-supervised learning has any disadvantages and in what ways semi-supervised learning is better than it.

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ANSWER

Answered 2021-Sep-08 at 06:35

I think that the best way to illustrate this problem is to cite the great Yann LeCun:

If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning (RL).

The different types of ML can be very good or not depending on the case. For example, for robotics or autonomous driving problems, RL would be the ideal solution given the nature of these algorithms. However, for a recommender system or a stock price predictor, you could probably find better (and simpler) solutions in supervised and unsupervised learning.

Supervised learning is very different from supervised and unsupervised learning in that it needs to be defined in terms of agent, states, and environment, rather than simply data (and labels in the case of supervised learning). Therefore, you will need those elements and define the interactions between them very carefully to train a good and reliable system that, as I mentioned above, might not be the most optimal (or even feasible) solution for the problem you are trying to solve.

Source https://stackoverflow.com/questions/69097885

QUESTION

Iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature

Asked 2020-Dec-04 at 01:03

I am trying to adapt this COLA repo to my audio dataset which I have in a local folder. I mainly change file contrastive.py to adapt method _get_ssl_task_data() to my new database.

However, I get an error triggered from model.fit (which calls my model.train_step(data) method below). I tried to fix this error by modifying data shape inside train_step but without any success.

I am not sure if this is an error because of shape or data type incompatibility or because I need to add more things to adapt my graph. Does anyone please know what's wrong with my code ? how can I replace the use of tf.Tensor in my case if this is really the issue ?

Here's the content of contrastive.py:

...

ANSWER

Answered 2020-Dec-04 at 01:03

The prolem was simply because my preprocessing was returning an array instead of a tuple that is required in the graph. So, the solution was to use tensorflow dataset utils to create my entire pipeline from files. This is also more efficient and uses much less memory of course.

Source https://stackoverflow.com/questions/65059475

QUESTION

How to invert a PyTorch Embedding?

Asked 2020-Oct-25 at 17:19

I have an multi-task encoder/decoder model in PyTorch with a (trainable) torch.nn.Embedding embedding layer at the input.

In one particular task, I'd like to pre-train the model self-supervised (to re-construct masked input data) and use it for inference (to fill in gaps in data).

I guess for training time I can just measure loss as the distance between the input embedding and the output embedding... But for inference, how do I invert an Embedding to reconstruct the proper category/token the output corresponds to? I can't see e.g. a "nearest" function on the Embedding class...

...

ANSWER

Answered 2020-Oct-25 at 17:19

You can do it quite easily:

Source https://stackoverflow.com/questions/64523788

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install self-supervised

The implementation is based on PyTorch. Logging works on wandb.ai. See docker/Dockerfile. To get this dataset, take the original ImageNet and filter out this subset of classes. We do not use augmentations during testing, and loading big images with resizing on the fly is slow, so we can preprocess classifier train and test images. We recommend mogrify for it. First, you need to resize to 256 (just like torchvision.transforms.Resize(256)) and then crop to 224 (like torchvision.transforms.CenterCrop(224)). Finally, put the original images to train, and resized to clf and test.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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