How to Train Your MAML to Excel in Few-Shot Classification

1 INTRODUCTION . Meta-learning is a sub-field of machine learning that attempts to search for the best learning strategy as the learning experiences increases ( Thrun & Pratt , 2012 ; Lemke et al. , 2015 ) . Recent years have witnessed an abundance of new approaches on meta-learning ( Vanschoren , 2018 ; Hospedales et al. , 2020 ) , among which model-agnostic meta-learning ( MAML ) ( Finn et al. , 2017 ; Finn , 2018 ) is one of the most popular algorithms , owing to its “ model-agnostic ” nature to incorporate different model architectures and its principled formulation to be applied to different problems . Concretely , MAML aims to learn a good model initialization ( through the outer loop optimization ) , which can then be quickly adapted to novel tasks given few examples ( through the inner loop optimization ) . However , in few-shot classification ( Vinyals et al. , 2016 ; Snell et al. , 2017 ) which many meta-learning algorithms are dedicated to , MAML ’ s performance has been shown to fall far behind ( Wang et al. , 2019 ; Chen et al. , 2019 ; Triantafillou et al. , 2020 ) . In this paper , we take a closer look at MAML on few-shot classification . The standard setup involves two phases , meta-training and meta-testing , in which MAML learns the model initialization during meta-training and applies it during meta-testing . In both phases , MAML receives multiple N -way Kshot tasks . Each task is an N -class classification problem provided with K labeled support examples per class . After the ( temporary ) inner loop optimization using the support examples , the updated model from the initialization is then evaluated on the query examples of the same N classes . The loss calculated on the query examples during mete-training is used to optimize the meta-parameters ( i.e. , the model initialization ) through the outer loop . For consistency , we mainly study the scenario where meta-training and meta-testing use the same number of gradient steps in the inner loop optimization . More specifically , what MAML learns in few-shot classification is the initialization of an N -class classifier . Without loss of generality , we denote a classifier by ŷ = argmaxc∈ [ N ] w > c fφ ( x ) , where fφ is the feature extractor on an example x , and { wc } Nc=1 are the weight vectors in the linear classifier head . We use θ to represent the collection of meta-parameters { φ , w1 , · · · , wN } . Our first observation is that MAML needs a large number of gradient steps in the inner loop . For example , on MiniImageNet ( Vinyals et al. , 2016 ) and TieredImageNet ( Ren et al. , 2018a ) , MAML ’ s accuracy improves along with the increased number of gradient steps and achieves the highest around 15 ∼ 20 steps , which are much larger than the conventional usage of MAML ( Antoniou et al. , 2019 ) . We attribute this to the behavior of the model initialization learned from mutually-exclusive tasks ( Yin et al. , 2020 ) , which , without any further inner loop optimization , performs at the chance level ( i.e. , 100 N % accuracy ) on query examples , not only for the meta-testing tasks but also for the meta-training tasks . In other words , the initialized model needs many gradient steps to attain a high accuracy . Our second observation is that MAML is sensitive to the permutations of class label assignments during meta-testing . Concretely , when an N -way K-shot task arrives , MAML pairs the learned initialization of wc with the corresponding class label c ∈ [ N ] of that task . The issue resides in the “ meaning ” of c in a task . In the standard setup , each N -way task is created by drawing N classes from a bigger pool of semantic classes ( e.g. , “ dog ” , “ cat ” , “ bird ” , etc . ) , followed by a random label re-assignment into c ∈ { 1 , · · · , N } . In other words , the same set of N semantic classes can be labeled totally differently into { 1 , · · · , N } and thus be paired with { wc } Nc=1 differently . Taking a five-way task for example , there are 5 ! = 120 permutations to pair the same set of five semantic classes to the linear classifiers . In some of them , a class “ dog ” may be assigned to c = 1 ; in some others , it may be assigned to c 6= 1 . While this randomness has been shown crucial in meta-training to help MAML prevent over-fitting ( Rajendran et al. , 2020 ; Yao et al. , 2021 ; Yin et al. , 2020 ) , we find that it makes the meta-testing phase unstable . Specifically , different permutations can lead to drastically different meta-testing accuracy — on average , the best permutation for each five-way one-shot task has ∼ 15 % higher accuracy than the worst permutation , on both datasets . Building upon this observation , we investigate multiple approaches to make MAML permutationinvariant , either in the meta-testing phase alone or in both phases . We find that a simple solution — meta-training only a single vector w and using it to initialize the N linear classifiers { wc } Nc=1 — performs the best . We name this approach UNICORN-MAML , as illustrated in Figure 1 ( b ) . Concretely , at the beginning of the inner loop , UNICORN-MAML duplicates w into wc , ∀c ∈ [ N ] . After the inner loop optimization , the meta-gradients with respect to { wc } Nc=1 are aggregated to updatew in the outer loop . This design not only makes UNICORN-MAML permutation-invariant , but also ensures that no single model can solve all tasks at once without inner loop optimization , to prevent memorization over-fitting ( Yin et al. , 2020 ) . On MiniImageNet ( Vinyals et al. , 2016 ) , TieredImageNet ( Ren et al. , 2018a ) , and CUB datasets ( Wah et al. , 2011 ) , UNICORN-MAML performs on a par with or even outperforms state-of-the-art few-shot classification algorithms , while preserving the simplicity of MAML without adding any extra network modules or learning strategies . 2 MAML FOR FEW-SHOT CLASSIFICATION . 2.1 PROBLEM DEFINITION . The goal of few-shot classification is to construct a classifier using limited labeled examples . The challenge is the potential over-fitting or poor generalization problem . Following ( Vinyals et al. , 2016 ) , we define a few-shot classification problem as an N -way K-shot task , which has N classes and each class has K labeled support examples . We denote the labeled support set by S = { ( xi , yi ) } N×Ki=1 ; each ( xi , yi ) is a pair of an input and a label , where yi ∈ { 1 , 2 , · · · , N } = [ N ] . The value of K is small , e.g. , K = 1 or K = 5 . To evaluate the quality of the resulting classifier , each task is associated with a query set Q , which is composed of examples of the same N classes . The core idea of meta-learning for few-shot classification is to sample few-shot tasks T = ( S , Q ) from a set of “ base ” classes , of which we have ample examples per class . Meta-learning then learns the ability of “ how to build a classifier using limited data ” from these tasks . After this meta-training phase , we then proceed to the meta-testing phase to tackle the true few-shot tasks that are composed of examples from “ novel ” classes . By default , the “ novel ” and “ base ” classes are disjoint . It is worth noting that the total number of “ base ” ( and “ novel ” ) classes is usually larger than N ( see subsection 2.3 ) . Thus , to construct an N -way K-shot task in each phase , one usually first samples N classes from the corresponding set of classes , and randomly re-labels each sampled class by an index c ∈ [ N ] . This randomness results in the so-called mutually-exclusive tasks ( Yin et al. , 2020 ) . In this paper , we will use base ( novel ) and meta-training ( meta-testing ) classes interchangeably . 2.2 MODEL-AGNOSTIC META-LEARNING ( MAML ) . As introduced in section 1 , MAML aims to learn the initialization of an N -way classifier , such that when provided with the support set S of an N -way K-shot task , the classifier can be quickly and robustly updated to perform well on the task ( i.e. , classify the query set Q well ) . Let us denote a classifier by ŷ = hθ ( x ) = argmaxc∈ [ N ] w > c fφ ( x ) , where fφ is the feature extractor , { wc } Nc=1 are the weight vectors of the classification head , and θ = { φ , w1 , · · · , wN } collects the parameters of both . MAML evaluates hθ on S and uses the gradient to update θ into θ′ , so that hθ′ can be applied to Q . This procedure is called the inner loop optimization , which usually takes M gradient steps . θ′ ← θ for m ∈ [ M ] do ( 1 ) θ′ = θ′ − α∇θL ( S , θ′ ) Here , L ( S , θ′ ) = ∑ ( x , y ) ∈S ` ( hθ′ ( x ) , y ) is the loss computed on examples of S and α is the learning rate ( or step size ) . The cross-entropy loss is commonly used for ` . As suggested in the original MAML paper ( Finn et al. , 2017 ) and ( Antoniou et al. , 2019 ) , M is usually set to a small integer ( e.g. , ≤ 5 ) . For ease of notation , let us denote the output θ′ after M gradient steps by θ′ = InLoop ( S , θ , M ) . To learn the initialization θ , MAML leverages the few-shot tasks sampled from the base classes . Let us denote by p ( T ) the distribution of tasks from the base classes , where each task is a pair of support and query sets ( S , Q ) , MAML aims to minimize the following meta-learning objective w.r.t . θ : ∑ ( S , Q ) ∼p ( T ) L ( Q , θ′S ) = ∑ ( S , Q ) ∼p ( T ) L ( Q , InLoop ( S , θ , M ) ) . ( 2 ) Namely , MAML aims to find a shared θ among tasks , which , after inner loop updates using S , can lead to a small loss on the query set Q . ( We add the subscript S to θ′ to show that θ′S depends on S . ) To optimize Equation 2 , MAML applies stochastic gradient descent ( SGD ) but at the task level . That is , at every iteration , MAML samples a task T = ( S , Q ) and computes the meta-gradient w.r.t . θ : ∇θL ( Q , θ′S ) = ∇θL ( Q , InLoop ( S , θ , M ) ) . ( 3 ) In practice , one may sample a mini-batch of tasks and compute the mini-batch meta-gradient w.r.t . θ to optimize θ . This SGD for θ is known as the outer loop optimization for MAML . It is worth noting that calculating the gradient in Equation 3 can impose considerable computational and memory burdens because it involves a gradient through a gradient ( along the inner loop but in a backward order ) ( Finn et al. , 2017 ) . Thus in practice , it is common to apply the first-order approximation ( Finn et al. , 2017 ; Nichol et al. , 2018 ) , i.e. , ∇θL ( Q , θ′S ) ≈ ∇θ′SL ( Q , θ ′ S ) . For additional related work on meta-learning and few-shot learning , please see Appendix A .

Summary: This paper is an interesting "re"search on a classical meta-learning method MAML. It shows that both the inner update iterations and task label assignment have a clear influence on the performance of MAML. It raises some questions and uses a set of in-depth experiments to draw conclusions.