Uniform Sampling over Episode Difficulty

1 Introduction . Large amounts of high-quality data have been the key for the success of deep learning algorithms . Furthermore , factors such as data augmentation and sampling affect model performance significantly . Continuously collecting and curating data is a resource ( cost , time , storage , etc . ) intensive process . Hence , recently , the machine learning community has been exploring methods for performing transfer-learning from large datasets to unseen tasks with limited data . A popular genre of these approaches is called meta-learning few-shot approaches , where , in addition to the limited data from the task of interest , a large dataset of disjoint tasks is available for ( pre- ) training . These approaches are prevalent in the area of computer vision [ 31 ] and reinforcement learning [ 6 ] . A key component of these methods is the notion of episodic training , which refers to sampling tasks from the larger dataset for training . By learning to solve these tasks correctly , the model can generalize to new tasks . However , sampling for episodic training remains surprisingly understudied despite numerous methods and applications that build on it . To the best of our knowledge , only a handful of works [ 69 , 55 , 33 ] explicitly considered the consequences of sampling episodes . In comparison , stochastic [ 46 ] and mini-batch [ 4 ] sampling alternatives have been thoroughly analyzed from the perspectives of optimization [ 17 , 5 ] , information theory [ 27 , 9 ] , and stochastic processes [ 68 , 66 ] , among many others . Building a similar understanding of sampling for episodic training will help theoreticians and practitioners develop improved sampling schemes , and is thus of crucial importance to both . In this paper , we explore many sampling schemes to understand their impact on few-shot methods . Our work revolves around the following fundamental question : what is the best way to sample episodes ? Our focus will be restricted to image classification in the few-shot learning setting – where “ best ” is taken to mean “ higher transfer accuracy of unseen episodes ” – and leave analyses and applications to other areas for future work . ∗Equal contributions ; work done while at Amazon Web Services . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . Contrary to prior work , our experiments indicate that sampling uniformly with respect to episode difficulty yields higher classification accuracy – a scheme originally proposed to regularize metric learning [ 62 ] . To better understand these results , we take a closer look at the properties of episodes and what makes them difficult . Building on this understanding , we propose a method to approximate different sampling schemes , and demonstrate its efficacy on several standard few-shot learning algorithms and datasets . Concretely , we make the following contributions : • We provide a detailed empirical analysis of episodes and their difficulty . When sampled randomly , we show that episode difficulty ( approximately ) follows a normal distribution and that the difficulty of an episode is largely independent of several modeling choices including the training algorithm , the network architecture , and the training iteration . • Leveraging our analysis , we propose simple and universally applicable modifications to the episodic sampling pipeline to approximate any sampling scheme . We then use this scheme to thoroughly compare episode sampling schemes – including easy/hard-mining , curriculum learning , and uniform sampling – and report that sampling uniformly over episode difficulty yields the best results . • Finally , we show that sampling matters for few-shot classification as it improves transfer accuracy for a diverse set of popular [ 53 , 20 , 15 , 41 ] and state-of-the-art [ 64 ] algorithms on standard and cross-domain benchmarks . 2 Preliminaries . 2.1 Episodic sampling and training . We define episodic sampling as subsampling few-shot tasks ( or episodes ) from a larger base dataset [ 7 ] . Assuming the base dataset admits a generative distribution , we sample an episode in two steps2 . First , we sample the episode classes Cτ from class distribution p ( Cτ ) ; second , we sample the episode ’ s data from data distribution p ( x , y | Cτ ) conditioned on Cτ . This gives rise to the following log-likelihood for a model lθ parameterized by θ : L ( θ ) = E τ∼q ( · ) [ log lθ ( τ ) ] , ( 1 ) where q ( τ ) is the episode distribution induced by first sampling classes , then data . In practice , this expectation is approximated by sampling a batch of episodes B each with their set τQ of query samples . To enable transfer to unseen classes , it is also common to include a small set τS of support samples to provide statistics about τ . This results in the following Monte-Carlo estimator : L ( θ ) ≈ 1 |B| ∑ τ∈B 1 |τQ| ∑ ( x , y ) ∈τQ log lθ ( y | x , τS ) , ( 2 ) where the data in τQ and τS are both distributed according to p ( x , y | Cτ ) . In few-shot classification , the n-way k-shot setting corresponds to sampling n = |Cτ | classes , each with k = |τS |n support data points . The implicit assumption in the vast majority of few-shot methods is that both classes and data are sampled with uniform probability – but are there better alternatives ? We carefully examine these underlying assumptions in the forthcoming sections . 2.2 Few-shot algorithms . We briefly present a few representative episodic learning algorithms . A more comprehensive treatment of few-shot algorithms is presented in Wang et al . [ 59 ] and Hospedales et al . [ 24 ] . A core question in few-shot learning lies in evaluating ( and maximizing ) the model likelihood lθ . These algorithms can be divided in two major families : gradient-based methods , which adapt the model ’ s parameters to the episode ; and metric-based methods , which compute similarities between support and query samples in a learned embedding space . Gradient-based few-shot methods are best illustrated through Model-Agnostic Meta-Learning [ 15 ] ( MAML ) . The intuition behind MAML is to learn a set of initial parameters which can quickly 2We use the notation for a probability distribution and its probability density function interchangeably . specialize to the task at hand . To that end , MAML computes lθ by adapting the model parameters θ via one ( or more ) steps of gradient ascent and then computes the likelihood using the adapted parameters θ′ . Concretely , we first compute the likelihood pθ ( y | x ) using the support set τS , adapt the model parameters , and then evaluate the likelihood : lθ ( y | x , τS ) = pθ′ ( y | x ) s.t . θ′ = θ + α∇θ ∑ ( x , y ) ∈τS log pθ ( y | x ) , where α > 0 is known as the adaptation learning rate . A major drawback of training with MAML lies in back-propagating through the adaptation phase , which requires higher-order gradients . To alleviate this computational burden , Almost No Inner Loop [ 41 ] ( ANIL ) proposes to only adapt the last classification layer of the model architecture while tying the rest of the layers across episodes . They empirically demonstrate little classification accuracy drop while accelerating training times four-fold . Akin to ANIL , metric-based methods also share most of their parameters across tasks ; however , their aim is to learn a metric space where classes naturally cluster . To that end , metric-based algorithms learn a feature extractor φθ parameterized by θ and classify according to a non-parametric rule . A representative of this family is Prototypical Network [ 53 ] ( ProtoNet ) , which classifies query points according to their distance to class prototypes – the average embedding of a class in the support set : lθ ( y | x , τS ) = exp ( −d ( φθ ( x ) , φyθ ) ) ∑ y′∈Cτ exp ( −d ( φθ ( x ) , φy ′ θ ) ) s.t . φcθ = 1k ∑ ( x , y ) ∈τS y=c φθ ( x ) , where d ( · , · ) is a distance function such as the Euclidean distance or the negative cosine similarity , and φcθ is the class prototype for class c. Other classification rules include support vector clustering [ 32 ] , neighborhood component analysis [ 30 ] , and the earth-mover distance [ 67 ] . 2.3 Episode difficulty . Given an episode τ and likelihood function lθ , we define episode difficulty to be the negative loglikelihood incurred on that episode : Ωlθ ( τ ) = − log lθ ( τ ) , which is a surrogate for how hard it is to classify the samples in τQ correctly , given lθ and τS . By definition , this choice of episode difficulty is tied to the choice of the likelihood function lθ . Dhillon et al . [ 11 ] use a similar surrogate as a means to systematically report few-shot performances . We use this definition because it is equivalent to the loss associated with the likelihood function lθ on episode τ , which is readily available at training time . 3 Methodology . In this section , we describe the core assumptions and methodology used in our study of sampling methods for episodic training . Our proposed method builds on importance sampling [ 21 ] ( IS ) which we found compelling for three reasons : ( i ) IS is well understood and solidly grounded from a theoretical standpoint , ( ii ) IS is universally applicable thus compatible with all episodic training algorithms , and ( iii ) IS is simple to implement with little requirement for hyper-parameter tuning . Why should we care about episodic sampling ? A back-of-the-envelope calculation3 suggests that there are on the order of 10162 different training episodes for the smallest-scale experiments in Section 5 . Since iterating through each of them is infeasible , we ought to express some preference over which episodes to sample . In the following , we describe a method that allows us to specify this preference . 3.1 Importance sampling for episodic training . Let us assume that the sampling scheme described in Section 2.1 induces a distribution q ( τ ) over episodes . We call it the proposal distribution , and assume knowledge of its density function . We wish 3For a base dataset with N classes and K input-output pairs per class , there are a total of ( N n ) ( K k ) n possible episodes that can be created when sampling k pairs each from n classes . Algorithm 1 : Episodic training with Importance Sampling Input : target ( p ) and proposal ( q ) distributions , likelihood function lθ , optimizer OPT . Randomly initialize model parameters θ. repeat Sample a mini-batch B of episodes from q ( τ ) . for each episode τ in mini-batch B do Compute episode likelihood : lθ ( τ ) . Compute importance weight : w ( τ ) = p ( τ ) q ( τ ) . end for Aggregate : L ( θ ) ← ∑ τ∈B w ( τ ) log lθ ( τ ) . Compute effective sample size ESS ( B ) . Update model parameters : θ ← OPT ( L ( θ ) ESS ( B ) ) . until parameters θ have converged . to estimate the expectation in Eq . ( 1 ) when sampling episodes according to a target distribution p ( τ ) of our choice , rather than q ( τ ) . To that end , we can use an importance sampling estimator which simply re-weights the observed values for a given episode τ by w ( τ ) = p ( τ ) q ( τ ) , the ratio of the target and proposal distributions : E τ∼p ( · ) [ log lθ ( τ ) ] = E τ∼q ( · ) [ w ( τ ) log lθ ( τ ) ] . The importance sampling identity holds whenever q ( τ ) has non-zero density over the support of p ( τ ) , and effectively allows us to sample from any target distribution p ( τ ) . A practical issue of the IS estimator arises when some values of w ( τ ) become much larger than others ; in that case , the likelihoods lθ ( τ ) associated with mini-batches containing heavier weights dominate the others , leading to disparities . To account for this effect , we can replace the mini-batch average in the Monte-Carlo estimate of Eq . ( 2 ) by the effective sample size ESS ( B ) [ 29 , 34 ] : E τ∼p ( · ) [ log lθ ( τ ) ] ≈ 1 ESS ( B ) ∑ τ∈B w ( τ ) log lθ ( τ ) s.t . ESS ( B ) = ( ∑ τ∈B w ( τ ) ) 2∑ τ∈B w ( τ ) 2 , ( 3 ) where B denotes a mini-batch of episodes sampled according to q ( τ ) . Note that whenw ( τ ) is constant , we recover the standard mini-batch average setting as ESS ( B ) = |B| . Empirically , we observed that normalizing with the effective sample size avoided instabilities . This method is summarized in Algorithm 1 .

Summary: Meta-learning (few-shot learning) algorithms, in general, equally treat all tasks for the training process. Tasks are sampled uniformly at random. This paper proposes a novel meta-learning framework that controls the task sampling probabilities according to difficulties. This paper measures the difficulties of tasks using the negative log-likelihood and leverages the difficulties to introduce different weights to tasks. The main idea of the learning framework is that the prior of the difficulties follows a normal distribution while the target sampling distribution could be uniform or some other interesting forms. Then, important sampling provides different weights to different tasks. From experiments, the uniform target distribution shows good results.