Code of the paper "Multi-Task Meta-Learning Modification with Stochastic Approximation".

Last update: Jan 05, 2022

Overview

Multi-Task Meta-Learning Modification with Stochastic Approximation

This repository contains the code for the paper
"Multi-Task Meta-Learning Modification with Stochastic Approximation".

Dependencies

This code has been tested on Ubuntu 16.04 with Python 3.8 and PyTorch 1.8.

To install the required dependencies:

pip install -r requirements.txt

Usage

To reproduce the results on benchmarks described in our article, use the following scripts. To vary types of the experiments, change the parameters of the scripts responsible for benchmark dataset, shot and way (e.g. miniImageNet 1-shot 5-way or CIFAR-FS 5-shot 2-way).

MAML

Multi-task modification (MTM) for Model-Agnostic Meta-Learning (MAML) (Finn et al., 2017).

Multi-task modifications for MAML are trained on top of baseline MAML model which has to be trained beforehand.

To train MAML (reproduced) on miniImageNet 1-shot 2-way benchmark, run:

python maml/train.py ./datasets/ \
    --run-name reproduced-miniimagenet \
    --dataset miniimagenet \
    --num-ways 2 \
    --num-shots 1 \
    --num-steps 5 \
    --num-epochs 300 \
    --use-cuda \
    --output-folder ./results

To train MAML MTM SPSA-Track on miniImageNet 1-shot 2-way benchmark, run:

python maml/train.py ./datasets/ \
    --run-name mini-imagenet-mtm-spsa-track \
    --load "./results/reproduced-miniimagenet/model.th" \
    --dataset miniimagenet \
    --num-ways 2 \
    --num-shots 1 \
    --num-steps 5 \
    --task-weighting spsa-track \
    --normalize-spsa-weights-after 100 \
    --num-epochs 40 \
    --use-cuda \
    --output-folder ./results

To train MAML (reproduced) on tieredImageNet 1-shot 2-way benchmark, run:

python maml/train.py ./datasets/ \
    --run-name reproduced-tieredimagenet \
    --dataset tieredimagenet \
    --num-ways 2 \
    --num-shots 1 \
    --num-steps 5 \
    --num-epochs 300 \
    --use-cuda \
    --output-folder ./results

To train MAML MTM SPSA on tieredImageNet 1-shot 2-way benchmark, run:

python maml/train.py ./datasets/ \
    --run-name tiered-imagenet-mtm-spsa \
    --load "./results/reproduced-tieredimagenet/model.th" \
    --dataset tieredimagenet \
    --num-ways 2 \
    --num-shots 1 \
    --num-steps 5 \
    --task-weighting spsa-delta \
    --normalize-spsa-weights-after 100 \
    --num-epochs 40 \
    --use-cuda \
    --output-folder ./results

To train MAML (reproduced) on FC100 5-shot 5-way benchmark, run:

python maml/train.py ./datasets/ \
    --run-name reproduced-fc100 \
    --dataset fc100 \
    --num-ways 5 \
    --num-shots 5 \
    --num-steps 5 \
    --num-epochs 300 \
    --use-cuda \
    --output-folder ./results

To train MAML MTM SPSA-Coarse on FC100 5-shot 5-way benchmark, run:

python maml/train.py ./datasets/ \
    --run-name fc100-mtm-spsa-coarse \
    --load "./results/reproduced-fc100/model.th" \
    --dataset fc100 \
    --num-ways 5 \
    --num-shots 5 \
    --num-steps 5 \
    --task-weighting spsa-per-coarse-class \
    --num-epochs 40 \
    --use-cuda \
    --output-folder ./results

To train MAML (reproduced) on CIFAR-FS 1-shot 5-way benchmark, run:

python maml/train.py ./datasets/ \
    --run-name reproduced-cifar \
    --dataset cifarfs \
    --num-ways 5 \
    --num-shots 1 \
    --num-steps 5 \
    --num-epochs 600 \
    --use-cuda \
    --output-folder ./results

To train MAML MTM Inner First-Order on CIFAR-FS 1-shot 5-way benchmark, run:

python maml/train.py ./datasets/ \
    --run-name cifar-mtm-inner-first-order \
    --load "./results/reproduced-cifar/model.th" \
    --dataset cifarfs \
    --num-ways 5 \
    --num-shots 1 \
    --num-steps 5 \
    --task-weighting gradient-novel-loss \
    --use-inner-optimizer \
    --num-epochs 40 \
    --use-cuda \
    --output-folder ./results

To train MAML MTM Backprop on CIFAR-FS 1-shot 5-way benchmark, run:

python maml/train.py ./datasets/ \
    --run-name cifar-mtm-backprop \
    --load "./results/reproduced-cifar-5shot-5way/model.th" \
    --dataset cifarfs \
    --num-ways 5 \
    --num-shots 1 \
    --num-steps 5 \
    --task-weighting gradient-novel-loss \
    --num-epochs 40 \
    --use-cuda \
    --output-folder ./results

To test any of the above-described benchmarks, run:

python maml/test.py ./results/path-to-config/config.json --num-steps 10 --use-cuda

For instance, to test MAML MTM SPSA-Track on miniImageNet 1-shot 2-way benchmark, run:

python maml/test.py ./results/mini-imagenet-mtm-spsa-track/config.json --num-steps 10 --use-cuda

Prototypical Networks

Multi-task modification (MTM) for Prototypical Networks (ProtoNet) (Snell et al., 2017).

To train ProtoNet MTM SPSA-Track with ResNet-12 backbone on miniImageNet 1-shot 5-way benchmark, run:

python protonet/train.py \
    --dataset miniImageNet \
    --network ResNet12 \
    --tracking \
    --train-shot 1 \
    --train-way 5 \
    --val-shot 1 \
    --val-way 5

To test ProtoNet MTM SPSA-Track with ResNet-12 backbone on miniImageNet 1-shot 5-way benchmark, run:

python protonet/test.py --dataset miniImageNet --network ResNet12 --shot 1 --way 5

To train ProtoNet MTM Backprop with 64-64-64-64 backbone on CIFAR-FS 1-shot 2-way benchmark, run:

python protonet/train.py \
    --dataset CIFAR_FS \
    --train-weights \
    --train-weights-layer \
    --train-shot 1 \
    --train-way 2 \
    --val-shot 1 \
    --val-way 2

To test ProtoNet MTM Backprop with 64-64-64-64 backbone on CIFAR-FS 1-shot 5-way benchmark, run:

python protonet/test.py --dataset CIFAR_FS --shot 1 --way 2

To train ProtoNet MTM Inner First-Order with 64-64-64-64 backbone on FC100 10-shot 5-way benchmark, run:

python protonet/train.py \
    --dataset FC100 \
    --train-weights \
    --train-weights-opt \
    --train-shot 10 \
    --train-way 5 \
    --val-shot 10 \
    --val-way 5

To test ProtoNet MTM Inner First-Order with 64-64-64-64 backbone on FC100 10-shot 5-way benchmark, run:

python protonet/test.py --dataset FC100 --shot 10 --way 5

To train ProtoNet MTM SPSA with 64-64-64-64 backbone on tieredImageNet 5-shot 2-way benchmark, run:

python protonet/train.py \
    --dataset tieredImageNet \
    --train-shot 5 \
    --train-way 2 \
    --val-shot 5 \
    --val-way 2

To test ProtoNet MTM SPSA with 64-64-64-64 backbone on tieredImageNet 5-shot 2-way benchmark, run:

python protonet/test.py --dataset tieredImageNet --shot 5 --way 2

Acknowledgments

Our code uses some dataloaders from Torchmeta.

Code in maml folder is based on the extended implementation from Torchmeta and pytorch-maml. The code has been updated so that baseline scores more closely follow those of the original MAML paper.

Code in protonet folder is based on the implementation from MetaOptNet. All .py files in this folder except for dataloaders.py and optimize.py were adopted from this implementation and modified afterwards. A copy of Apache License, Version 2.0 is available in protonet folder.

Code of the paper "Multi-Task Meta-Learning Modification with Stochastic Approximation".

Related tags

Overview

Multi-Task Meta-Learning Modification with Stochastic Approximation

Dependencies

Usage

MAML

Prototypical Networks

Acknowledgments

Owner

Andrew

Official PyTorch implementation for paper Context Matters: Graph-based Self-supervised Representation Learning for Medical Images

Bayesian Generative Adversarial Networks in Tensorflow

Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On

Official repository for the paper F, B, Alpha Matting

App for identification of various objects. Based on YOLO v4 tiny architecture

PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)

AAAI 2022 paper - Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

Diabetes-Feature-Engineering - A machine learning model that can predict whether people have diabetes when their characteristics are specified

A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations

CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution

Understanding the Generalization Benefit of Model Invariance from a Data Perspective

CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

This is a repo of basic Machine Learning!

Real-time Joint Semantic Reasoning for Autonomous Driving

The pytorch implementation of the paper "text-guided neural image inpainting" at MM'2020

The King is Naked: on the Notion of Robustness for Natural Language Processing

Plato: A New Framework for Federated Learning Research

OpenDILab RL Kubernetes Custom Resource and Operator Lib

NAACL2021 - COIL Contextualized Lexical Retriever

"Graph Neural Controlled Differential Equations for Traffic Forecasting", AAAI 2022