Does MAML Only Work via Feature Re-use? A Data Set Centric Perspective

Overview

Does-MAML-Only-Work-via-Feature-Re-use-A-Data-Set-Centric-Perspective

Does MAML Only Work via Feature Re-use? A Data Set Centric Perspective

Installing

Standard pip instal [Recommended]

TODO

If you are going to use a gpu the do this first before continuing (or check the offical website: https://pytorch.org/get-started/locally/):

pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html

Otherwise, just doing the follwoing should work.

pip install automl

If that worked, then you should be able to import is as follows:

import automl

Manual installation [Development]

To use library first get the code from this repo (e.g. fork it on github):

git clone [email protected]/brando90/automl-meta-learning.git

Then install it in development mode in your python env with python >=3.9 (read modules_in_python.md to learn about python envs in uutils). E.g. create your env with conda:

conda create -n metalearning python=3.9
conda activate metalearning

Then install it in edibable mode and all it's depedencies with pip in the currently activated conda environment:

pip install -e ~/automl-meta-learning/automl-proj-src/

since the depedencies have not been written install them:

pip install -e ~/ultimate-utils/ultimate-utils-proj-src

then test as followsing:

python -c "import uutils; print(uutils); uutils.hello()"
python -c "import meta_learning; print(meta_learning)"
python -c "import meta_learning; print(meta_learning); meta_learning.hello()"

output should be something like this:

hello from uutils __init__.py in: (metalearning) brando~/automl-meta-learning/automl-proj-src ❯ python -c "import meta_learning; print(meta_learning)" (metalearning) brando~/automl-meta-learning/automl-proj-src ❯ python -c "import meta_learning; print(meta_learning); meta_learning.hello()" hello from torch_uu __init__.py in: ">
(metalearning) brando~/automl-meta-learning/automl-proj-src ❯ python -c "import uutils; print(uutils); uutils.hello()"

       
        

hello from uutils __init__.py in:

        
         

(metalearning) brando~/automl-meta-learning/automl-proj-src ❯ python -c "import meta_learning; print(meta_learning)"

         
          
(metalearning) brando~/automl-meta-learning/automl-proj-src ❯ python -c "import meta_learning; print(meta_learning); meta_learning.hello()"

          
           

hello from torch_uu __init__.py in:

            
           
          
         
        
       

Reproducing Results

TODO

Citation

B. Miranda, Y.Wang, O. Koyejo.
Does MAML Only Work via Feature Re-use? A Data Set Centric Perspective. 
(Planned Release Date December 2021).
https://drive.google.com/file/d/1cTrfh-Tg39EnbI7u0-T29syyDp6e_gjN/view?usp=sharing

https://drive.google.com/file/d/1cTrfh-Tg39EnbI7u0-T29syyDp6e_gjN/view?usp=sharing

You might also like...
EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers

EntityQuestions This repository contains the EntityQuestions dataset as well as code to evaluate retrieval results from the the paper Simple Entity-ce

EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers

EntityQuestions This repository contains the EntityQuestions dataset as well as code to evaluate retrieval results from the the paper Simple Entity-ce

[CVPR 2022 Oral] Versatile Multi-Modal Pre-Training for Human-Centric Perception
[CVPR 2022 Oral] Versatile Multi-Modal Pre-Training for Human-Centric Perception

Versatile Multi-Modal Pre-Training for Human-Centric Perception Fangzhou Hong1  Liang Pan1  Zhongang Cai1,2,3  Ziwei Liu1* 1S-Lab, Nanyang Technologic

The official repo for OC-SORT: Observation-Centric SORT on video Multi-Object Tracking. OC-SORT is simple, online and robust to occlusion/non-linear motion.
The official repo for OC-SORT: Observation-Centric SORT on video Multi-Object Tracking. OC-SORT is simple, online and robust to occlusion/non-linear motion.

OC-SORT Observation-Centric SORT (OC-SORT) is a pure motion-model-based multi-object tracker. It aims to improve tracking robustness in crowded scenes

In the case of your data having only 1 channel while want to use timm models

timm_custom Description In the case of your data having only 1 channel while want to use timm models (with or without pretrained weights), run the fol

This is our ARTS test set, an enriched test set to probe Aspect Robustness of ABSA.
This is our ARTS test set, an enriched test set to probe Aspect Robustness of ABSA.

This is the repository for our 2020 paper "Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis". Data We provide

 Open-Set Recognition: A Good Closed-Set Classifier is All You Need
Open-Set Recognition: A Good Closed-Set Classifier is All You Need

Open-Set Recognition: A Good Closed-Set Classifier is All You Need Code for our paper: "Open-Set Recognition: A Good Closed-Set Classifier is All You

Script that receives an Image (original) and a set of images to be used as
Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of images as "pixels"

picinpics Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of

 OpenGAN: Open-Set Recognition via Open Data Generation
OpenGAN: Open-Set Recognition via Open Data Generation

OpenGAN: Open-Set Recognition via Open Data Generation ICCV 2021 (oral) Real-world machine learning systems need to analyze novel testing data that di

Owner
null
PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML)

pytorch-maml This is a PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML): https://arxiv

Kate Rakelly 516 Jan 5, 2023
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR 2022)

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR2022)[paper] Authors: Chenhang He, Ruihuang Li, Shuai Li, L

Billy HE 141 Dec 30, 2022
GB-CosFace: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classification

GB-CosFace: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classification This is the official pytorch implementation of t

Alibaba Cloud 5 Nov 14, 2022
Team nan solution repository for FPT data-centric competition. Data augmentation, Albumentation, Mosaic, Visualization, KNN application

FPT_data_centric_competition - Team nan solution repository for FPT data-centric competition. Data augmentation, Albumentation, Mosaic, Visualization, KNN application

Pham Viet Hoang (Harry) 2 Oct 30, 2022
Apply a perspective transformation to a raster image inside Inkscape (no need to use an external software such as GIMP or Krita).

Raster Perspective Apply a perspective transformation to bitmap image using the selected path as envelope, without the need to use an external softwar

s.ouchene 19 Dec 22, 2022
StyleGAN-Human: A Data-Centric Odyssey of Human Generation

StyleGAN-Human: A Data-Centric Odyssey of Human Generation Abstract: Unconditional human image generation is an important task in vision and graphics,

stylegan-human 762 Jan 8, 2023
You Only Look One-level Feature (YOLOF), CVPR2021, Detectron2

You Only Look One-level Feature (YOLOF), CVPR2021 A simple, fast, and efficient object detector without FPN. This repo provides a neat implementation

qiang chen 273 Jan 3, 2023
a practicable framework used in Deep Learning. So far UDL only provide DCFNet implementation for the ICCV paper (Dynamic Cross Feature Fusion for Remote Sensing Pansharpening)

UDL UDL is a practicable framework used in Deep Learning (computer vision). Benchmark codes, results and models are available in UDL, please contact @

Xiao Wu 11 Sep 30, 2022
Object-Centric Learning with Slot Attention

Slot Attention This is a re-implementation of "Object-Centric Learning with Slot Attention" in PyTorch (https://arxiv.org/abs/2006.15055). Requirement

Untitled AI 72 Jan 2, 2023
NUANCED is a user-centric conversational recommendation dataset that contains 5.1k annotated dialogues and 26k high-quality user turns.

NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions Overview NUANCED is a user-centric conversational recommen

Facebook Research 18 Dec 28, 2021