Continual Learning with Delayed Feedback

Most of the artificial neural networks are using the benefit of labeled datasets whereas in human brain , the learning is often unsupervised . The feedback or a label for a given input or a sensory stimuli is not often available instantly . After some time when brain gets the feedback , it updates its knowledge . That ’ s how brain learns . Moreover , there is no training or testing phase . Human learns continually . This work proposes a model-agnostic continual learning framework which can be used with neural networks as well as decision trees to incorporate continual learning . Specifically , this work investigates how delayed feedback can be handled . In addition , a way to update the Machine Learning models with unlabeled data is proposed . Promising results are received from the experiments done on neural networks and decision trees . 1 INTRODUCTION . The high representational capacity of neural networks as well as the discovery of good training methods , and the access to fast processing units resulted in super human accuracy in various artificial intelligent tasks . Supervised image classification results has improved to super human levels by various convolutional neural network ( CNN ) architectures such as AlexNet ( Krizhevsky et al. , 2012 ) , VGG ( Simonyan & Zisserman , 2014 ) , GoogleNet ( Szegedy et al. , 2015 ) , and ResNetHe et al . ( 2016 ) . Most of the CNNs are learned with supervision.The limitation of labeled dataset opens the path for unsupervised learning . Moreover , human brain often does continual learning in an unsupervised manner . As an example , take an infant . It does not know most of the things in the environment . But it sees everything . Whenever somebody gives some detail or a label about the object that the baby has seen , it updates its knowledge . This work names this detail or label as delayed feedback . Hence , the key difference in CNNs is that we feed everything with labels at the start which does not occur in human brain . In addition , humans learn continually . The distinction of training and testing is not realized in actual learning of human . Figure 1 explains a delayed feedback scenario . Suppose an infant sees 3 different cars . But it does not know anything about the car . After sometime , somebody shows a new car and labels it as a car . Now the infant learns that the previous unknowns vehicles should be cars . In this case , the baby does not know any car before seeing these objects . This scenario can be extended to different types or shapes of cars as well where baby knows about one set of cars and how it can learn other different cars . Unsupervised learning is discussed predominantly in 2 categories . One is reconstructing the original data using auto-encoders and the latent representations are used for the downstream tasks ( Hinton et al. , 2011 ) . Especially this resulted in good dimensionality reduction techniques such as t-SNE ( Maaten & Hinton , 2008 ) . The second one is instead of reconstructing the given data , the model learns a contrastive task which can produce latent representations which are useful for other AI tasks ( Hjelm et al. , 2018 ) , ( Schneider et al. , 2019 ) . Both categories can find better representations to capture the variability of the data in the input space . In either ways , the task specific information extraction is not possible due to learning the same data or a contrastive task . The area of updating a supervised model parameters using an unlabeled data is not explored as per our knowledge . In this work , an idea is proposed to update the model to improve the accuracy over some new data . Mainly , two contributions are presented in this paper . 1 . A way to handle delayed feedback is presented and implemented by maintaining a queue to store some recent data of samples . This concept introduces the same delayed feedback of actual brain in artificial neural networks . 2 . Instead of having training and testing phase , a new continual learning model is proposed which can update the model even with unlabeled data . 2 RELATED WORK . 2.1 CONTINUAL LEARNING . Neuroscience literature has widely spoken about continual learning ( ( Hassabis et al. , 2017 ) , Parisi et al . ( 2019 ) ) . However , the literature on continual learning in artificial neural network is not studied deeply and still at the inception level . Continual learning is mainly discussed via resolving the issue of Catastrophic Forgetting ( ( Kirkpatrick et al. , 2017 ) ) . Catastrophic forgetting occurs when a neural network forgets most part of the previously learnt task while learning a new task . The distributional shift in inputs is not clearly modeled using neural networks . Hence , neural networks perform badly with the old tasks . Superposition of models into a single neural network ( ( Cheung et al. , 2019 ) ) and neural network pruning ( ( Golkar et al. , 2019 ) ) are proposed to do continual learning . 2.2 DELAYED FEEDBACK . Delayed Feedback is an important phenomenon in human learning . Brain encounters different objects routinely without knowing the actual identifier of what it is . When some stimulus gives the label , brain updates the knowledge . If there is no signal for a certain period of time , then the object seen will be erased from memory . This mechanism is named as delayed feedback . Speech and visual delayed feedback is discussed previously ( ( Lee , 1950 ) , ( Smith , 1962 ) ) . But the effect of delayed feedback is vastly unexplored in artificial neural networks . Data hungry supervised algorithms achieved state of the art results in popular AI tasks such as image classification , recognition , and segmentation , but the availability of labeled dataset is always limited than the abundant unlabeled data in the wild . The availability of abundant unlabeled dataset and delayed feedback can jointly build strong continuous learning models . 3 CONTINUAL LEARNING FRAMEWORK WHICH HANDLES DELAYED FEEDBACK . 3.1 BASIC MODEL . This work introduces 2 terms seeding and growing instead of training and testing . Seeding is the phase where the initial learning occurs . This is the phase where the model takes advantage and learns from labeled data . Growing is the phase where the model learns information from new unlabeled data and updates the model . The learnt neural network of a classification problem can be seen as a function which produces confidence values ( f ( X ) = Y ) . Initial function f is improved to f ′ in the conventional training of a neural network , same thing happens here with seeding . But the function f is fixed in conventional neural networks in testing phase where as in growing phase of the continual learning framework the model parameters are updated with the framework rules . Hence mostly a new function ( f ′′ ) will be seen after every new inputs . The figure 2 illustrates how the frame work works in growing phase . When a new sample ( Xi ) comes in ( for an image classification problem , Xi is an image ) , the model will predict confidence value for the input . Confidence value is the probability vector ( Yi ) for each sample ( In a neural network , Yi is the output from softmax layer ) . The input ( Xi ) and the confidence value vector ( Yi ) are inserted to a bounded queue . If the queue is not full , the next sample will be processed and appended to the queue . When the queue is full , an element from queue is removed based on the following criteria . In addition , one hot encoding is randomly created to give the feedback ( Z0 ) . 1 . Queue is searched for confidence value ( Yi ) which has the lowest distributional distance or the highest similarity with the one hot encoding ( Z0 ) . Bhattacharyya coefficient is used to measure the similarity between two distributions . 2 . Now the best Y1 is selected to match the given feedback . Y1 has a corresponding image ( X1 ) . Now the model is updated with the sample ( X1 , Z0 ) . The element ( X1 , Y1 ) is removed from the queue . 3 . Assume the learning rate used for seeding is r. The learning rate used in growing phase is r ′ which is around 10 times smaller than r. When we update the model with sample ( X1 , Z0 ) , we use a higher learning rate ( r ′×s where s > 1 ) if the bhattacharyya coefficient between Z0 and Y1 is in [ 0.5 , ] ( Here < 1 ) . If ymax > , the model is updated with a small learning rate ( r ′ /s where s > 1 ) . = 0.8 used in most of the experiments . 4 . If there are no samples with bhattacharrya coefficient ≥ 0.5 , then the oldest element in the queue is removed ( dequeue operation in the queue ) without any update to the model . The reason behind selecting a lower learning rate for high confidence vector is that the model is confident about the decision . Hence , updating with a higher learning rate has a higher probability of overfitting or fitting a wrong sample with high confidence . If the confidence values are in [ 0.5 , ] , then the model is not highly confident , but fair enough to say that these decisions will be correct . Hence , we are updating these samples with higher learning rate . Having a good initial model from seeding leads to improvement in accuracy . But starting with a bad model with respect to accuracy may result in models which are worse than initial model . Take the same infant example , if the infant is taught wrong things from the start , then it will make more decisions which are wrong . Similar thing can happen here as well . Figure 3 illustrates the update rules . If the distance in the input space is higher ( Iref and X1 ) , then no update is done . If the distance in the input space is lower , then the same update rule explained in section 3.1 is applied to the model . The update of the model is restricted by the input space similarity . This update rule increased the accuracy by a good margin . In the experiments , s = 5 is used update the model . Selecting a higher r ′ than r is not desirable because the probability of getting an inaccurate model increases with r ′ s which are greater than r. Seeding phase contains most of the updates to the model . It is seeded with several epochs whereas growing phase deals with a single update for each sample . If the initial learnt function from seeding is g , the growing phase functions will fall in a small manifold around g. Continual learning is different from online learning . In online learning , samples come with labels and the update is done instantly . But in continual learning , instant update is not done . Instead of updating the samples are kept in queue . When the queue is full , updates are done as described in this section . The learning is almost fixed in online learning at least for a certain period of time whereas in continual learning , it depends on the sample . Infact , machine learning models which have online version are easy to do continual learning since the updates can be done partially for the given samples .

Summary: The paper is quite hard to follow. The figures are very coarse and the problem definition is not given very well. The paper claims that it is about continual learning, but it does not give ANY experimental results on the continual learning benchmarks. The paper seems to be dealing with an online learning with unlabeled data.