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

This git repo contains the implementation of my ML project on Heart Disease Prediction

Introduction This git repo contains the implementation of my ML project on Heart Disease Prediction. This is a real-world machine learning model/proje

Aryan Dutta 1 Feb 02, 2022
working repo for my xumx-sliCQ submissions to the ISMIR 2021 MDX

Music Demixing Challenge - xumx-sliCQ This repository is the GitHub mirror of my working submission repository for the AICrowd ISMIR 2021 Music Demixi

4 Aug 25, 2021
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection. CVPR 2022

PGNet Pyramid Grafting Network for One-Stage High Resolution Saliency Detection. CVPR 2022, CVPR 2022 (arXiv 2204.05041) Abstract Recent salient objec

CVTEAM 109 Dec 05, 2022
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

11.4k Jan 09, 2023
Official implementation for "Low-light Image Enhancement via Breaking Down the Darkness"

Low-light Image Enhancement via Breaking Down the Darkness by Qiming Hu, Xiaojie Guo. 1. Dependencies Python3 PyTorch=1.0 OpenCV-Python, TensorboardX

Qiming Hu 30 Jan 01, 2023
EEGEyeNet is benchmark to evaluate ET prediction based on EEG measurements with an increasing level of difficulty

Introduction EEGEyeNet EEGEyeNet is a benchmark to evaluate ET prediction based on EEG measurements with an increasing level of difficulty. Overview T

Ard Kastrati 23 Dec 22, 2022
Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019

Class-Balanced Loss Based on Effective Number of Samples Tensorflow code for the paper: Class-Balanced Loss Based on Effective Number of Samples Yin C

Yin Cui 546 Jan 08, 2023
Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"

L2F - Learning to Forget for Meta-Learning Sungyong Baik, Seokil Hong, Kyoung Mu Lee Source code for CVPR 2020 paper "Learning to Forget for Meta-Lear

Sungyong Baik 29 May 22, 2022
Converting CPT to bert form for use

cpt-encoder 将CPT转成bert形式使用 说明 刚刚刷到又出了一种模型:CPT,看论文显示,在很多中文任务上性能比mac bert还好,就迫不及待想把它用起来。 根据对源码的研究,发现该模型在做nlu建模时主要用的encoder部分,也就是bert,因此我将这部分权重转为bert权重类型

黄辉 1 Oct 14, 2021
An efficient PyTorch implementation of the winning entry of the 2017 VQA Challenge.

Bottom-Up and Top-Down Attention for Visual Question Answering An efficient PyTorch implementation of the winning entry of the 2017 VQA Challenge. The

Hengyuan Hu 731 Jan 03, 2023
An excellent hash algorithm combining classical sponge structure and RNN.

SHA-RNN Recurrent Neural Network with Chaotic System for Hash Functions Anonymous Authors [摘要] 在这次作业中我们提出了一种新的 Hash Function —— SHA-RNN。其以海绵结构为基础,融合了混

Houde Qian 5 May 15, 2022
Continuous Conditional Random Field Convolution for Point Cloud Segmentation

CRFConv This repository is the implementation of "Continuous Conditional Random Field Convolution for Point Cloud Segmentation" 1. Setup 1) Building c

Fei Yang 8 Dec 08, 2022
Implementation of H-UCRL Algorithm

Implementation of H-UCRL Algorithm This repository is an implementation of the H-UCRL algorithm introduced in Curi, S., Berkenkamp, F., & Krause, A. (

Sebastian Curi 25 May 20, 2022
OOD Generalization and Detection (ACL 2020)

Pretrained Transformers Improve Out-of-Distribution Robustness How does pretraining affect out-of-distribution robustness? We create an OOD benchmark

littleRound 57 Jan 09, 2023
A symbolic-model-guided fuzzer for TLS

tlspuffin TLS Protocol Under FuzzINg A symbolic-model-guided fuzzer for TLS Master Thesis | Thesis Presentation | Documentation Disclaimer: The term "

69 Dec 20, 2022
The Official TensorFlow Implementation for SPatchGAN (ICCV2021)

SPatchGAN: Official TensorFlow Implementation Paper "SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation"

39 Dec 30, 2022
Official code for the paper: Deep Graph Matching under Quadratic Constraint (CVPR 2021)

QC-DGM This is the official PyTorch implementation and models for our CVPR 2021 paper: Deep Graph Matching under Quadratic Constraint. It also contain

Quankai Gao 55 Nov 14, 2022
Implicit Graph Neural Networks

Implicit Graph Neural Networks This repository is the official PyTorch implementation of "Implicit Graph Neural Networks". Fangda Gu*, Heng Chang*, We

Heng Chang 48 Nov 29, 2022
Baseline powergrid model for NY

Baseline-powergrid-model-for-NY Table of Contents About The Project Built With Usage License Contact Acknowledgements About The Project As the urgency

Anderson Energy Lab at Cornell 6 Nov 24, 2022
[ICLR2021oral] Rethinking Architecture Selection in Differentiable NAS

DARTS-PT Code accompanying the paper ICLR'2021: Rethinking Architecture Selection in Differentiable NAS Ruochen Wang, Minhao Cheng, Xiangning Chen, Xi

Ruochen Wang 86 Dec 27, 2022