CS826 Transfer Learning PDF

CS826 Deep Learning Theory and Practice Transfer Learning Edited by Nur Naim, Oct 2021  Transfer learning  Transductive vs Inductive Transfer Learning  Pre-training  Freezing and Fine-tuning  Pre-trained Network  Transfer learning approach for Natural Language Processing (NLP)  Deep...

CS826 Deep Learning Theory and Practice Transfer Learning Edited by Nur Naim, Oct 2021  Transfer learning  Transductive vs Inductive Transfer Learning  Pre-training  Freezing and Fine-tuning  Pre-trained Network  Transfer learning approach for Natural Language Processing (NLP)  Deep learning methods are data-hungry  >50K data items needed for training  The distributions of the source and target data must be the same  Labeled data in the target domain may be limited  This problem is typically addressed with transfer learning training test (labeled) (unlabeled) Classifier 85.5 % New York New York Times Times Ack. From Jing Jiang’s slides  training test (labeled) (unlabeled) Classifier 64.1 % Labeled data not available! New York Times Reuters Ack. From Jing Jiang’s slides  train test ideal setting NYT NYT 85.5% Classifier New York Times New York Times realistic setting Reuters NYT 64.1 Classifier % Reuters New York Times Ack. From Jing Jiang’s slides   Multi-task learning  Learn several related tasks at the same time with shared representations  Single P(x) but multiple output variables  Transfer learning  Two stage domain adaptation: select generalizable features from training domains and specific features from test domain 10/49  Inductive  Adapt existing supervised training model on new labeled dataset  Regression  Transductive  Adapt existing supervised training model on new unlabeled dataset  Classification, Regression  Unsupervised  Adapt existing unsupervised training model on new unlabeled dataset  Clustering, Dimensionality Reduction  Transductive transfer  No labeled target domain data available  Focus of most transfer research in NLP (Natural Language Processing)  E.g. Domain adaptation  Inductive transfer  Labeled target domain data available  Goal: improve performance on the target task by training on other task(s)  Jointly training on >1 task (multi-task learning)  Pre-training (e.g. word embeddings)  Labeled Data Model Test set Unlabeled Data Transductive Applications and problems – Labeled examples are scarce but unlabeled data are abundant – Web page classification, review ratings prediction  Self-training  Give labels to unlabeled data  Generative models  Unlabeled data help get better estimates of the parameters  Transductive SVM  Maximize the unlabeled data margin  Graph-based algorithms  Construct a graph based on labeled and unlabeled data, propagate labels along the paths  Distance learning  Map the data into a different feature space where they could be better separated  Improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned.  Weight initialization for CNN  Two major strategies  ConvNet as fixed feature extractor  Fine-tuning the ConvNet  Lots of data, time, resources needed to train and tune a neural network from scratch  An ImageNet deep neural net can take weeks to train and fine-tune from scratch.  Unless you have 256 GPUs, possible to achieve in 1 hour  Cheaper, faster way of adapting a neural network by exploiting their generalization properties Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks [Oquab et al. CVPR 2014]  The ImageNet dataset was created by a group of professors and researchers at Princeton, Stanford, and UNC Chapel Hill.  ImageNet was originally formed with the goal of populating the WordNet hierarchy with roughly 500-1000 images per concept.  Images for each concept were gathered by querying search engines and passing candidate images through a validation step on Amazon Mechanical Turk.  The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is the most commonly used subset of the ImageNet dataset. Within this subset: ~ImageNet contains 1,281,167 training images ~ImageNet contains 50,000 validation images ~ImageNet contains 100,000 test images ~ImageNet contains 1000 object classes  New dataset is small and similar to original dataset.  train a linear classifier on the CNN codes  New dataset is large and similar to the original dataset  fine-tune through the full network  New dataset is small but very different from the original dataset  SVM/softmax classifier from activations somewhere earlier in the network  New dataset is large and very different from the original dataset  fine-tune through the entire network  Overly aggressive fine-tuning causes catastrophic forgetting  Too cautious fine-tuning leads to slow convergence and overfitting  Proposed approach: gradual unfreezing  First unfreeze the last layer and fine-tune the unfrozen layer for one epoch  Then unfreeze the next lower frozen layer and fine-tune all unfrozen layers  Repeat until we fine-tune all layers until convergence in the last iteration  The combination of discriminative fine-tuning, skewed triangular learning rates and gradual unfreezing leads to best performance  Pre-Trained Models for Image Classification  VGG-16  ResNet50  Inceptionv3  EfficientNet  The VGG-16 is one of the most popular pre-trained models for image classification.  Introduced in the famous ILSVRC 2014 Conference, it was and remains THE model to beat even today.  Developed at the Visual Graphics Group at the University of Oxford, VGG-16 beat the standard of AlexNet and was quickly adopted by researchers and the industry for their image Classification Tasks.  A good transfer learning strategy is outlined as following steps:  Freezing the lower ConvNet blocks (blue) as fixed feature extractor. Take a ConvNet pretrained on ImageNet, remove the last fully-connected layers, then treat the rest of the ConvNet as a fixed feature extractor for the new dataset.  In an VGG16 network, this would compute a 4096-D vector for every image that contains the activations of the hidden layer immediately before the classifier. These features are termed as CNN codes.  Training the new fully-connected layers (green, aka. bottleneck layers). Extract the CNN codes for all images, train a linear classifier (e.g. Linear SVM or Softmax classifier) for the new dataset.  Fine-tuning the ConvNet. Replace and retrain the classifier on top of the ConvNet on the new dataset, but to also fine-tune the weights of the pre-trained network by continuing the back-propagation to part of the higher layers (yellow+green).  Spam filtering  Public email collection  personal inboxes  Intrusion detection  Existing types of intrusions  unknown types of intrusions  Sentiment analysis  Expert review articles blog review articles  The aim  To design learning methods that are aware of the training and test domain difference  Transfer learning  Adapt the classifiers learnt from the source domain to the new domain  https://filebox.ece.vt.edu/~jbhuang/teaching/ece6554/sp17/lectures/Lectu re_04_Supervised_Pretraining.pptx  https://ov-research.uwaterloo.ca/MSCI641/Week10_Transfer_learning.pptx  https://lisaong.github.io/mldds-courseware/03_TextImage/transfer- learning.slides.html  https://towardsdatascience.com/pre-trained-language-models-simplified- b8ec80c62217  https://ruder.io/state-of-transfer-learning-in-nlp/  https://lisaong.github.io/mldds-courseware/03_TextImage/transfer- learning.slides.html  Google (image) – VGG16, ImageNet, pre-trained, fine-tuning, transfer learning for NLP.