Codebase for the paper titled "Continual learning with local module selection"

Related tags

Deep LearningLMC
Overview

This repository contains the codebase for the paper Continual Learning via Local Module Composition.


Setting up the environemnt

Create a new conda environment and install the requirements.

conda create --name ENV python=3.7
conda activate ENV
pip install -r requirements.txt
pip install -e Utils/ctrl/
pip install Utils/nngeometry/

CTrL Benchmark

All experiments were run on Nvidia Quadro RTX 8000 GPUs. To run CTrL experiments use the following comands for different streams:

Stream S-

LMC (task agnostic)

python main_transfer.py --activate_after_str_oh=0 --momentum_bn 0.1 --track_running_stats_bn 1 --pr_name lmc_cr --shuffle_test 0 --init_oh=none --task_sequence s_minus --momentum_bn_decoder=0.1 --activation_structural=sigmoid --deviation_threshold=4 --depth=4 --epochs=100 --fix_layers_below_on_addition=0 --hidden_size=64 --lr=0.001 --mask_str_loss=1 --module_init=mean --multihead=gated_linear --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=20 --reg_factor=10  --running_stats_steps=100 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --temp=0.1 --wdecay=0.001

(test acc. 0.6863, 15 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --pr_name lmc_cr --copy_batchstats 1 --track_running_stats_bn 1 --task_sequence s_minus --gating MNTDP --shuffle_test 0 --epochs 100 --lr 1e-3 --wdecay 1e-3

(test acc. 0.667, 12 modules)

Stream S+

LMC

python main_transfer.py --activate_after_str_oh=0 --activation_structural=sigmoid --deviation_threshold=1.5 --early_stop_complete=0 --pr_name lmc_cr --epochs=100 --epochs_str_only_after_addition=1 --hidden_size=64 --init_oh=none --init_runingstats_on_addition=1 --keep_bn_in_eval_after_freeze=1 --lr=0.001 --module_init=most_likely --momentum_bn=0.1 --momentum_bn_decoder=0.1 --multihead=gated_linear --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=5 --reg_factor=10 --running_stats_steps=100 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --task_sequence=s_plus --temp=1 --wdecay=0.001

(test acc. 0.6244, 22 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --pr_name lmc_cr --copy_batchstats 1 --track_running_stats_bn 1 --task_sequence s_plus --gating MNTDP --shuffle_test 0 --epochs 100 --lr 1e-3 --wdecay 1e-3 --regenerate_seed 0

(test acc. 0.609, 18 modules)

Stream Sin

LMC

python main_transfer.py --activate_after_str_oh=0 --momentum_bn 0.1 --track_running_stats_bn 1 --pr_name lmc_cr --shuffle_test 0 --init_oh=none --task_sequence s_in --momentum_bn_decoder=0.1 --activation_structural=sigmoid --deviation_threshold=4 --depth=4 --epochs=100 --fix_layers_below_on_addition=0 --hidden_size=64 --lr=0.001 --mask_str_loss=1 --module_init=most_likely --multihead=gated_linear --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=20 --reg_factor=10  --running_stats_steps=100 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --temp=0.1 --wdecay=0.001

(test acc. 0.7081, 21 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --pr_name lmc_cr --copy_batchstats 1 --track_running_stats_bn 1 --task_sequence s_in --gating MNTDP --shuffle_test 0 --epochs 100 --lr 1e-3 --wdecay 1e-3 --regenerate_seed 0

(test acc. 0.6646, 15 modules)

Stream Sout

LMC

python main_transfer.py --activate_after_str_oh=0 --momentum_bn 0.1 --track_running_stats_bn 1 --pr_name lmc_cr --shuffle_test 0 --init_oh=none --task_sequence s_out --momentum_bn_decoder=0.1 --activation_structural=sigmoid --deviation_threshold=4 --depth=4 --epochs=100 --fix_layers_below_on_addition=0 --hidden_size=64 --lr=0.001 --mask_str_loss=1 --module_init=mean --multihead=gated_linear --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=20 --reg_factor=10  --running_stats_steps=100 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --temp=0.1 --wdecay=0.001

(test acc. 0.5849, 15 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --pr_name lmc_cr --copy_batchstats 1 --track_running_stats_bn 1 --task_sequence s_out --gating MNTDP --shuffle_test 0 --epochs 100 --lr 1e-3 --wdecay 0 --regenerate_seed 0

(test acc. 0.6567, 11 modules)

Stream Spl

LMC

python main_transfer.py --activate_after_str_oh=0 --activation_structural=sigmoid --pr_name lmc_cr --deviation_threshold=1.5 --early_stop_complete=0 --epochs=100 --hidden_size=64 --init_oh=none --init_runingstats_on_addition=0 --keep_bn_in_eval_after_freeze=1 --lr=0.001 --module_init=most_likely --momentum_bn=0.1 --momentum_bn_decoder=0.1 --multihead=gated_linear --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=10 --reg_factor=10 --running_stats_steps=100 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --task_sequence=s_pl --temp=1 --regenerate_seed 0 --wdecay=0.001

(test acc. 0.6241, 19 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --pr_name lmc_cr --copy_batchstats 1 --track_running_stats_bn 1 --task_sequence s_pl --gating MNTDP --shuffle_test 0 --epochs 100 --lr 1e-3 --wdecay 1e-4 --regenerate_seed 0

(test acc. 0.6391, 18 modules)


Stream Slong30 -- 30 tasks

LMC (task aware)

python main_transfer.py --activate_after_str_oh=0 --activation_structural=sigmoid --deviation_threshold=1.5 --epochs=50 --hidden_size=64 --init_oh=none --keep_bn_in_eval_after_freeze=1 --lr=0.001 --module_init=most_likely --momentum_bn_decoder=0.1 --multihead=gated_linear --n_tasks=100 --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=5 --reg_factor=1 --running_stats_steps=50 --seed=180 --str_prior_factor=1 --str_prior_temp=0.01 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=0 --task_sequence=s_long30 --temp=1 --wdecay=0.001

(test acc. 62.44, 50 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --epochs=50 --hidden_size=64 --lr=0.001 --module_init=most_likely --multihead=gated_linear --n_tasks=100 --seed=180 --task_sequence=s_long30 --wdecay=0.001

(test acc. 64.58, 64 modules)


Stream Slong -- 100 tasks

LMC (task aware)

python main_transfer.py --activate_after_str_oh=0 --activation_structural=sigmoid --deviation_threshold=4 --epochs=100 --hidden_size=64 --init_oh=none --keep_bn_in_eval_after_freeze=1 --lr=0.001 --module_init=most_likely --momentum_bn_decoder=0.1 --multihead=gated_linear --n_tasks=100 --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=5 --reg_factor=1 --running_stats_steps=50 --seed=180 --str_prior_factor=1 --str_prior_temp=0.01 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=0 --task_sequence=s_long --temp=1 --pr_name s_long_cr --wdecay=0

(test acc. 63.88, 32 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --n_tasks 100 --hidden_size 64 --searchspace topdown --keep_bn_in_eval_after_freeze 1 --pr_name s_long_cr --copy_batchstats 1 --track_running_stats_bn 1 --wand_notes correct_MNTDP --task_sequence s_long --gating MNTDP --shuffle_test 0 --epochs 50 --lr 1e-3 --wdecay 1e-3

(test acc. 68.92, 142 modules)


OOD generalization experiments

LMC

python main_transfer.py --regenerate_seed 0 --deviation_threshold=8 --epochs=50 --pr_name lmc_cr --hidden_size=64 --keep_bn_in_eval_after_freeze=0 --lr=0.001 --module_init=none --momentum_bn_decoder=0.1 --normalize_data=1 --optmize_structure_only_free_modules=0 --projection_phase_length=10 --no_projection_phase 0 --reg_factor=10 --running_stats_steps=1000 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=linear_no_act --task_sequence=s_ood --temp=1 --wdecay=0 --task_agnostic_test=0

EWC

python main_transfer.py --epochs=50 --ewc=1000 --hidden_size=256 --keep_bn_in_eval_after_freeze=0 --lr=0.001 --module_init=none --pr_name lmc_cr --multihead=usual --normalize_data=1  --task_sequence=s_ood --use_structural=0 --wdecay=0 --projection_phase_length=0

MNTDP

python main_transfer_mntdp.py --epochs=50 --regenerate_seed 0 --hidden_size=64 --keep_bn_in_eval_after_freeze=0 --pr_name lmc_cr --lr=0.01 --module_init=none --multihead=usual --normalize_data=1 --task_sequence=s_ood --use_structural=0 --wdecay=0

LMC (no projetion)

python main_transfer.py --regenerate_seed 0 --deviation_threshold=8 --epochs=50 --pr_name lmc_cr --hidden_size=64 --keep_bn_in_eval_after_freeze=0 --lr=0.001 --module_init=none --momentum_bn_decoder=0.1 --normalize_data=1 --optmize_structure_only_free_modules=0 --projection_phase_length=0 --no_projection_phase 1 --reg_factor=10 --running_stats_steps=1000 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=linear_no_act --task_sequence=s_ood --temp=1 --wdecay=0

Plug and play (combining independently trained modular learners)

python main_plug_and_play.py --activate_after_str_oh=0 --activation_structural=sigmoid --deviation_threshold=1.5 --early_stop_complete=0 --epochs=100 --epochs_str_only_after_addition=1 --pr_name lmc_cr --hidden_size=64 --init_oh=none --init_runingstats_on_addition=1 --keep_bn_in_eval_after_freeze=1 --lr=0.001 --module_init=mean --momentum_bn=0.1 --momentum_bn_decoder=0.1 --multihead=gated_linear --n_tasks=3 --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=5 --reg_factor=10 --running_stats_steps=10 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --task_sequence=s_pnp_comp --temp=1 --wdecay=0.001

A list of hyperparameters used for other baselines can be found in the baselines.txt file.


References

Owner
Oleksiy Ostapenko
Oleksiy Ostapenko
Fashion Entity Classification

Fashion-Entity-Classification - Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grays

ADITYA SHAH 1 Jan 04, 2022
PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

PyStan NOTE: This documentation describes a BETA release of PyStan 3. PyStan is a Python interface to Stan, a package for Bayesian inference. Stan® is

Stan 229 Dec 29, 2022
Official Implementation of Neural Splines

Neural Splines: Fitting 3D Surfaces with Inifinitely-Wide Neural Networks This repository contains the official implementation of the CVPR 2021 (Oral)

Francis Williams 56 Nov 29, 2022
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

Robust Video Matting (RVM) English | 中文 Official repository for the paper Robust High-Resolution Video Matting with Temporal Guidance. RVM is specific

flow-dev 2 Aug 21, 2022
Source code for our paper "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations"

Source code for our paper "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations" this repository is maintained by bo

Yuhan Liu 24 Nov 29, 2022
Github for the conference paper GLOD-Gaussian Likelihood OOD detector

FOOD - Fast OOD Detector Pytorch implamentation of the confernce peper FOOD arxiv link. Abstract Deep neural networks (DNNs) perform well at classifyi

17 Jun 19, 2022
A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions

A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions Kapoutsis, A.C., Chatzichristofis,

Athanasios Ch. Kapoutsis 5 Oct 15, 2022
The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

machen 11 Nov 27, 2022
Code for "Hierarchical Skills for Efficient Exploration" HSD-3 Algorithm and Baselines

Hierarchical Skills for Efficient Exploration This is the source code release for the paper Hierarchical Skills for Efficient Exploration. It contains

Facebook Research 38 Dec 06, 2022
SelfRemaster: SSL Speech Restoration

SelfRemaster: Self-Supervised Speech Restoration Official implementation of SelfRemaster: Self-Supervised Speech Restoration with Analysis-by-Synthesi

Takaaki Saeki 46 Jan 07, 2023
ADB-IP-ROTATION - Use your mobile phone to gain a temporary IP address using ADB and data tethering

ADB IP ROTATE This an Python script based on Android Debug Bridge (adb) shell sc

Dor Bismuth 2 Jul 12, 2022
[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers Created by Xumin Yu*, Yongming Rao*, Ziyi Wang, Zuyan Liu, Jiwen Lu, Jie Zhou

Xumin Yu 317 Dec 26, 2022
[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"

GCA Source code for Graph Contrastive Learning with Adaptive Augmentation (WWW 2021) For example, to run GCA-Degree under WikiCS, execute: python trai

Big Data and Multi-modal Computing Group, CRIPAC 97 Jan 07, 2023
An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models.

DeepNER An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models. This repository contains complex Deep

Derrick 9 May 30, 2022
Official PyTorch implementation of paper: Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation (ICCV 2021 Oral Presentation)

SML (ICCV 2021, Oral) : Official Pytorch Implementation This repository provides the official PyTorch implementation of the following paper: Standardi

SangHun 61 Dec 27, 2022
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica

Kuan-Lin (Jason) Chen 2 Oct 02, 2022
Implementation of parameterized soft-exponential activation function.

Soft-Exponential-Activation-Function: Implementation of parameterized soft-exponential activation function. In this implementation, the parameters are

Shuvrajeet Das 1 Feb 23, 2022
Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Arsenii Senya Ashukha 80 Sep 17, 2022
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution (CVPR2021)

MASA-SR Official PyTorch implementation of our CVPR2021 paper MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Re

DV Lab 126 Dec 20, 2022
Clustering with variational Bayes and population Monte Carlo

pypmc pypmc is a python package focusing on adaptive importance sampling. It can be used for integration and sampling from a user-defined target densi

45 Feb 06, 2022