Comments (3)
Documentation is definitely incomplete, and PRs to add more documentation are welcome!
For example, usually when creating a meta-set one has a number of data-sets. How many data-sets are actually created? Usually in one episode one creates one data-set. Is this how your library does it or how?
In CombinationMetaDataset
(the most frequently used MetaDataset
), the number of datasets created corresponds to C(number of classes in the dataset, number of ways)
; the datasets are created by first picking number of ways
classes among the pool of possible classes in the dataset, and then images are sampled from these classes.
from pytorch-meta.
Documentation is definitely incomplete, and PRs to add more documentation are welcome!
For example, usually when creating a meta-set one has a number of data-sets. How many data-sets are actually created? Usually in one episode one creates one data-set. Is this how your library does it or how?
In
CombinationMetaDataset
(the most frequently usedMetaDataset
), the number of datasets created corresponds toC(number of classes in the dataset, number of ways)
; the datasets are created by first pickingnumber of ways
classes among the pool of possible classes in the dataset, and then images are sampled from these classes.
Awesome I will make sure to do PR's as I discover more uses for ur stuff. :)
Let me ask my question this way. Is sampling a batch of tasks (say 16) the usual definition of an episode?
A comment on the term episodic learning would be useful to have somewhere in the docs. Perhaps a small section of definitions for terms in meta-leanring would be helpful.
Some I can think of are:
- episode
- meta-batch
- support vs training set
- query vs test set
- meta-sets (meta train, meta val, meta test)
- the difference between data vs task distribution (e.g. mini-imagenet has 600 images)
Hope not to sound demanding, your library is already pretty awesome and it's highly appreciated :) ;) "
Hope I can help to make it better :D
from pytorch-meta.
Any contribution is welcome, thank you for your help!
Having a general definition for these terms would be great indeed, and the docs could use some tutorials to introduce some of these.
Let me ask my question this way. Is sampling a batch of tasks (say 16) the usual definition of an episode?
The way I see it, an episode means the dataset of one single task. Having a batch of tasks is really only there to reduce the variance of the gradient estimate during the outer-loop optimization. When doing episodic learning (inner-loop), you only get to see data from the one task you try to solve, you can't use information from other tasks.
from pytorch-meta.
Related Issues (20)
- Addition of validation per batch HOT 1
- Bug with dataparallel in Pytorch 1.7 + cu110
- Is not normalizing in the helper functions a problem?
- Can the code count the number of segmented targets?
- How to augment support set with torchmeta?
- How to retain the original labels of test/train targets? HOT 2
- meta-dataset support pytorch?
- Is it possible to create my own torchmeta data set using my own classification data set pytorch obj?
- Missing check_integrity import from torchvision.datasets.utils HOT 1
- Torchmeta downgrades the Torch and the Torchvision versions HOT 1
- compatability with next pytorch 1.12.1? HOT 3
- Download miniimagenet error HOT 2
- Is meta data set's fo proto maml available? HOT 1
- when i run the train.py ,there is a errer that cannot find the ordered-set
- how to download a pytorch version that is compatible with thorchmeta 1.8.0 HOT 3
- Torch meta can't import in colab HOT 1
- ERROR: ResolutionImpossible HOT 4
- Columns and DataType Not Explicitly Set on line 372 of tcga.py
- version problem HOT 1
- dataset links
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
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.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from pytorch-meta.