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View Code? Open in Web Editor NEWTrain Reversible Neural Network on mobile devices
License: BSD 2-Clause "Simplified" License
Train Reversible Neural Network on mobile devices
License: BSD 2-Clause "Simplified" License
ODT Todo:
NLP Todo:
Done:
Todo
Applications to leverage on-device training:
Speech (recognition and synthesis, adaptation:
Speech recognition: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/0007893.pdf
TTS:
https://openreview.net/pdf?id=rkzjUoAcFX
https://arxiv.org/pdf/1810.07217.pdf
http://people.csail.mit.edu/wnhsu/assets/pdf/is17_learning_v2_43.pdf
Dataset: https://ai.google/tools/datasets/libri-tts
Code: https://pytorch.org/hub/nvidia_deeplearningexamples_tacotron2/
Meta learning, few shot learning, domain adaptation:
Few shot image classification: https://openreview.net/pdf?id=HkxLXnAcFQ
Meta learning for supervised domain adaptation: https://arxiv.org/pdf/1711.02536.pdf
Learning to segment everything: https://arxiv.org/abs/1711.10370
Online adaptation for depth estimation: https://arxiv.org/pdf/1904.08462.pdf
Meta learning for RL:
Model based online adaptation: https://arxiv.org/pdf/1803.11347.pdf
https://arxiv.org/pdf/1812.07671.pdf
Continual learning:https://arxiv.org/pdf/1802.07569.pdf
Adaptive CV:
Problem formulation: Learn a model that can adapt to new classes/data distribution/tasks with a few target data
Data distribution (domain adaptation): Sim2Real, AmazonWeb -> Real, and so on
Few-shot: expanding to 100 shots. The goal is to fully close the performance gap with supervised learning, while reducing the amount of data and computation needed.
Milestones:
Literature search
Problem formulation:
Application scenarios
Determine how many shots
How to get source/target domain
How to benchmark
How to prepare data
Etc.
Algorithm development:
Given, say, 100 samples of target data, are we able to obtain the same accuracy as fully supervised learning? How many target data is needed?
Do existing few shot learning / domain adaptation algorithms solve this problem?
Baseline, baseline++, MAML, etc.
If they do not work well, can we propose new algorithms to solve this problem?
Move to mobile
People interested:
Xuanyu, Tianyuan
Adaptive TTS:
Problem: given a few samples of speech from a target person, transfer a TTS model to synthesize voices close to the target person
Milestones:
Literature search, identify a framework to start from
Reduce the model such that we can run inference on a mobile
Make the model to reversible so we can leverage our on-device training
Move to mobile
People:
Flora, Tianren, Bohan
Preliminary milstones:
#milestone 1: +batch norm + train cifar and check accuracy
#milestone 2: train imagenet, 50 accuracy, 1 week training time, 50 + accuracy
Potential directions for applications:
To do:
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