Online Meta-Learning via Learning with Layer-Distributed Memory

1 Introduction . Meta-learning or learning-to-learn is a paradigm that enables models to generalize to a distribution of tasks rather than specialize to just one task [ 1 , 2 ] . When encountering examples from a new task , we would like the model to adapt to the new task after seeing just a few samples . This is commonly achieved via episodic training of deep neural networks , where , in each episode , the network is exposed to a variety of inputs from the same distribution [ 3 , 4 ] , and the distribution shifts over episodes . The ability of deep networks to adapt to a new task within just a few samples or iterations is central to the application of meta-learning methods in few-shot and online learning scenarios [ 5 , 6 ] . A recent surge of interest directed towards meta-learning using neural networks has spurred development of a variety of methods [ 7–9 ] . In a standard episodic training framework , a network must adapt to a sampled task ( or collection of tasks ) and incurs a generalization loss for that task ( or collection ) ; this generalization loss is backpropagated to update the network weights . Methods differ in the underlying architecture and mechanisms they use to support adaptation . Strategies include using gradient descent in an inner loop , storing and updating prototypes , parameterizing update rules by another neural network , and employing neural memory [ 3 , 10–12 ] . Section 2 provides an overview . We focus on memory-based meta-learning , and specifically investigate the organization of neural memory for meta-learning . Motivating this focus is the generality and flexibility of memory-based approaches . Relying on memory for adaptation allows one to cast meta-learning as merely a learning problem using a straightforward loss formulation ( viewing entire episodes as examples ) and standard optimization techniques . The actual burden of adaptation becomes an implicit responsibility of the memory subsystem : the network must learn to use its persistent memory in a manner that facilitates task adaptation . This contrasts with explicit adaptation mechanisms such as stored prototypes . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . In this implicit adaptation setting , memory architecture plays a crucial role in determining what kind of adaptation can be learned . We experimentally evaluate the effectiveness of alternative neural memory architectures for meta-learning and observe particular advantages to distributing memory throughout a network . More specifically , we view the generic LSTM equations , Wx+Wh−1 , as adaptation induced by hidden states in activation space ( see Figure 1 ) . By distributing LSTM memory cells across the depth of the network , each layer is tasked with generating hidden states that are useful for adaptation . Such a memory organization is compatible with many standard networks , including CNNs , and can be achieved by merely swapping LSTM memory cells in place of existing filters . Our simple approach also contrasts with several existing memory-based meta-learning methods used in both generative and classification tasks [ 13–17 ] . These methods view memory as a means to store and retrieve useful inductive biases for task adaptation , and hence focus on designing better read and write protocols . They typically have a feature extractor that feeds into a memory network that performs adaptation , whereas our architecture makes no such distinction between stages . We test the efficacy of network architectures with distributed memory cells on online few-shot and continual learning tasks as in Santoro et al . [ 13 ] , Ren et al . [ 18 ] and Javed and White [ 6 ] . The online setting is challenging for two reasons : 1 ) It is empirically observed that networks are not well suited for training/adaptation with a batch size of one [ 19 ] ; 2 ) In this setting the model has to adapt to one image at a time step , thus having to deal with a prolonged adaptation phase . For these reasons , we see these tasks as suitable for evaluating the adaptation capabilities of the hidden states generated by the network . We empirically observe that our method outperforms strong gradient-based and prototypical baselines , delineating the efficacy of the local adaptation rule learnt by each layer . Particularly important is the distributed nature of our memory , which allows every network layer to adapt when provided with label information ; in comparison , restricting adaptability to only later network layers delivers far less compelling performance . These results suggest that co-design of memory architecture and metatraining strategies should be a primary consideration in the ongoing development of memory-based meta-learning . We further test our model in a harder online few-shot learning scenario , wherein the corresponding label to a sample arrives after a long delay [ 20 ] . Our method adapts seamlessly , without requiring any changes to the model , while , in this setting , other adaptation strategies struggle . These results highlight promising directions for advancing and simplifying meta-learning by relying upon distributed memory for adaptation . 2 Related Work . Early work on meta-learning introduces many relevant concepts . Schmidhuber [ 21 ] proposes using task specific weights , called fast weights , and weights that are adapted across tasks , called slow weights . Bengio et al . [ 2 ] updates the network via a learning rule which is parameterized by another neural network . Thrun [ 22 ] presents meta-learning in a life-long scenario , where the algorithm accrues information from the past experiences to adapt effectively for the task at hand . Hochreiter et al . [ 23 ] train a memory network to learn its own adaptation rule via just its recurrent states . These high level concepts can be seen in more recent methods . We group current meta-learning methods based on the nature of adaptation strategy and discuss them below . Gradient-based Adaptation Methods . Methods that adapt via gradients constitute a prominent class of meta-learning algorithms [ 9 ] . Model-agnostic meta-learning ( MAML ) [ 4 ] learns an initialization that can efficiently be adapted by gradient descent for a new task . Finn et al . [ 24 ] focus on learning a network that can use experience from previously seen tasks for current task adaptation . They adapt to the current task by using a network that is MAML pre-trained on the samples from the previous task . Nagabandi et al . [ 25 ] , Caccia et al . [ 26 ] perform online adaptation under non-stationary distributions , either by using a mixture model or by spawning a MAML pre-trained network when the input distribution changes . Javed and White [ 6 ] , Beaulieu et al . [ 27 ] employ a bi-level optimization routine similar to MAML , except the outer loop loss is catastrophic forgetting . They thereby learn representations that are robust to forgetting and accelerate future learning under online updates . Memory and Gradient-based Adaptation . Andrychowicz et al . [ 28 ] , Ravi and Larochelle [ 10 ] learn an update rule for network weights by transforming gradients via a LSTM , which outperforms human-designed and fixed SGD update rules . Munkhdalai and Yu [ 29 ] learn a transform that maps gradients to fast ( task specific ) weights , which are stored and retrieved via attention during evaluation . They update slow weights ( across task weights ) at the end of each task . Prototypical Methods . These methods learn an encoder which projects training data to a metric space , and obtain class-wise prototypes via averaging representations within the same class . Following this , test data is mapped to the same metric space , wherein classification is achieved via a simple rule ( e.g. , nearest neighbor prototype based on either euclidean distance or cosine similarity ) [ 5 , 30 , 31 ] . These methods are naturally amenable for online learning as class-wise prototypes can be updated in an online manner as shown by Ren et al . [ 18 ] . Memory-based Adaptation . Santoro et al . [ 13 ] design efficient read and write protocols for a Neural Turning Machine [ 32 ] for the purposes of online few-shot learning . Rae et al . [ 33 ] design sparse read and write operations , thereby making them scalable in both time and space . Ramalho and Garnelo [ 11 ] use logits generated by the model to decide if a certain sample is written to neural memory . Mishra et al . [ 7 ] employ an attention-based mechanism to perform adaptation , and use a CNN to generate features for the attention mechanism . Their model requires storing samples across all time steps explicitly , thereby violating the online learning assumption of being able to access each sample only once . All of these methods mainly focus on designing better memory modules either via using more recent attention mechanisms or by designing better read and write rules to neural memory . These methods typically use a CNN which is not adapted for the current task . Our approach differs from these methods , in that we study efficient organization of memory for both online few-shot learning and meta-learning more generally , and show that as a consequence of our distributed memory organization , the entire network is capable of effective adaptation when provided with relevant feedback . Kirsch and Schmidhuber [ 34 ] introduce an interesting form of weight sharing wherein LSTM cells ( with tied weights ) are distributed throughout the width and depth of the network , however each position has its own hidden state . Further , they have backward connections from the later layers to the earlier layers , enabling the network to implement its own learning algorithm or clone a humandesigned learning algorithm such as backprop . Both our model and theirs implement an adaptation strategy purely using the recurrent states . The difference , however , is in the nature of the adaptation strategy implemented in the recurrent states . Similar to conventional learning algorithms , their backward connections help propagate error from the last layer to the earlier layers . In our architecture , the feedback signal is presented as another input , propagated from the first layer to the last layer . In addition to being used in classification settings , Guez et al . [ 35 ] employ memory-based metalearning approach to perform adaptation for reinforcement learning tasks indicating the generality of using memory as a means for adaptation . Few-shot Semantic Segmentation . Few-shot segmentation methods commonly rely on using prototypes [ 36 , 37 ] , though recent approaches include gradient-based methods analogous to MAML [ 38 ] . The methods that use neural memory typically employ it in final network stages to fuse features of different formats for efficient segmentation : Li et al . [ 39 ] use ConvLSTMs [ 40 ] to fuse features from different stages of the network ; Valipour et al . [ 41 ] to fuse spatio-temporal features while segmenting videos ; Hu et al . [ 42 ] use a ConvLSTM to fuse features of query with the features of support set ; Azad et al . [ 43 ] use a bidirectional ConvLSTM to fuse segmentation derived from multiple scale space representations . We differ from these works in organization , use of , and information provided to memory module : 1 ) Memory is distributed across the network as the sole driver of adaptation ; 2 ) Label information is provided to assist with adaptation . Meta-learning Benchmarks . Caccia et al . [ 26 ] present benchmarks that measure the ability of a model to adapt to a new task , using the inductive biases that it has acquired over solving previously seen tasks . More specifically , the benchmark presents an online non-stationary stream of tasks , and the model ’ s ability to adapt to a new task at each time step is evaluated . Note that they do not measure the model ’ s ability to remember earlier tasks ; they only want the model to adapt well on a newly presented task . Antoniou et al . [ 44 ] present benchmarks for continual few-shot learning . The network is presented a number of few-shot tasks , one after the other , and then is expected to generalize even to the previously seen tasks . This is a challenging and interesting setup , in that , the network has to show robustness to catastrophic forgetting while learning from limited data . However , we are interested in evaluating the online adaptation ability of models , while Antoniou et al . [ 44 ] feed data in a batch setting . We follow experimental setup as in Javed and White [ 6 ] , where in , the model is required to remember inductive biases acquired over a longer time frame when compared to the experimental setup used by Antoniou et al . [ 44 ] .

Summary: In this paper the authors propose a distributed memory system to tackle the online few-shot setting. The system is based on layers of conv-LSTMs that take as input an image and the labels at previous steps to generate activations and store information.   Overall I think that the technical contribution is marginal. Both the system structure and the problem formulation make the method impractical and difficult to scale to more complex settings. Experiments are limited to small datasets and do not show the potential of the proposed solution. Limitations are not appropriately discussed and a better framing w.r.t. more recent online few-shot learning work is missing. In the current form the paper is not ready to be accepted. I am open to change my mind and increase my score if the authors provide a strong rebuttal and a satisfactory answer to the concerns I have outlined below.