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

Retinal Vessel Segmentation with Pixel-wise Adaptive Filters (ISBI 2022)

Yolov5 deepsort inference，使用YOLOv5+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中

PyTorch implementation of Barlow Twins.

Piotr - IoT firmware emulation instrumentation for training and research

This is the pytorch implementation for the paper: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation, which is accepted to ICCV2021.

Cards Against Humanity AI

A simple version for graphfpn

Safe Model-Based Reinforcement Learning using Robust Control Barrier Functions

OpenMMLab Model Deployment Toolset

Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)

JupyterLite demo deployed to GitHub Pages 🚀

Generalized Data Weighting via Class-level Gradient Manipulation

tmm_fast is a lightweight package to speed up optical planar multilayer thin-film device computation.

Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).

Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.

Convert ONNX model graph to Keras model format.

Multi-modal Content Creation Model Training Infrastructure including the FACT model (AI Choreographer) implementation.

“Data Augmentation for Cross-Domain Named Entity Recognition” (EMNLP 2021)