MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning
Authors: Nanyi Fei
Zhiwu Lu
Tao Xiang
Songfang Huang
Venue: ICLR 2021 Poster (2021)
Keywords: few-shot learning
episodic training
cross-episode attention

Abstract: Most recent few-shot learning (FSL) approaches are based on episodic training whereby each episode samples few training instances (shots) per class to imitate the test condition. However, this strict adhering to test condition has a negative side effect, that is, the trained model is susceptible to the poor sampling of few shots. In this work, for the first time, this problem is addressed by exploiting inter-episode relationships. Specifically, a novel meta-learning via modeling episode-level relationships (MELR) framework is proposed. By sampling two episodes containing the same set of classes for meta-training, MELR is designed to ensure that the meta-learned model is robust against the presence of poorly-sampled shots in the meta-test stage. This is achieved through two key components: (1) a Cross-Episode Attention Module (CEAM) to improve the ability of alleviating the effects of poorly-sampled shots, and (2) a Cross-Episode Consistency Regularization (CECR) to enforce that the two classifiers learned from the two episodes are consistent even when there are unrepresentative instances. Extensive experiments for non-transductive standard FSL on two benchmarks show that our MELR achieves 1.0%-5.0% improvements over the baseline (i.e., ProtoNet) used for FSL in our model and outperforms the latest competitors under the same settings.

Published as a conference paper at ICLR 2021
MELR: META-LEARNING VIA MODELING EPISODE- LEVEL RELATIONSHIPS FOR FEW-SHOT LEARNING
Nanyi Fei
School Information, Renmin University of China, Beijing, China seinanyi&rue edu.
Zhiwu Lu"
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China luzhiwu@ruc, edu. cn
Tao Xiang
Songfang Huang
University of Surrey Guildford, Surrey, UK t. xxiian@suurey..cc.u
Alibaba DAMO Academy
Hangzhou, China
songfang haaabbbabaaaaaaineeeaaaaaa com
ABSTRACT
Most recent few-shot learning (FSL) approaches are based on episodic training whereby each episode samples few training instances (shots) per class to imitate the test condition. However, this strict adhering to test condition has a negative side effect, that is, the trained model is susceptible to the poor sampling of few shots. In this work, for the first time, this problem is addressed by exploiting inter- episode relationships. Specifically, novel meta-learning via modeling episode- level relationships (MELR) framework is proposed. By sampling two episodes containing the same of classes for meta-training, MELR is designed to en- sure that the meta-learned model is robust against the presence of poorly-sampled shots in the meta-test stage. This is achieved through two key components: (I) Cross-EDe -ooss-Episode Attention Module (CEAM) to improve the ability of alleviating the effects of poorly-samplet shots, and (2) a Cross-Episode Consistency Regu- larization (CECR) to enforce that the two classifiers learned from the two episodes are consistent even when there are unrepresentative instances. Extensive experi- ments for non-transductive standard FSL on two benchmarks show that our MELR achieves 1.0%-5.09 improvements over the baseline (i.e., ProtoNet) used for FSL in model and outperforms the latest competitors under the same settings.
INTRODUCTION
Deep convolutional neural networks (CNNs) have achieved tremendous successes in wide range
of computer vision tasks including object recognition (Krizhevsky et al. 2012 Simonyan & Zisser-
man
2015 Russakovsky et al.) 2015 He et al.) 2016a) semantic segmentation (Long et al. 2015 Chen et al. 2018), and object detection (Ren et al.) 2015; Redmon et al.) 2016). For most visual recognition tasks, at least hundreds of labeled training images are required from each class for train- ing CNN model. However, collecting a large number of labeled training samples is costly and may even be impossible in real-life application scenarios (Antonie et al.) 2001 Yang et al. 2012). To reduce the reliance of deep neural networks on large amount of annotated training data, few-shot learning (FSL) has been studied [Vinyals al., 2016 Finn al. 2017 Snell al.) 201 2 Sung et al.) 2018), which aims to recognize set of novel classes with only few labeled samples by knowledge
transfer from a set of base classes with abundant samples.
"Corresponding author.

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Published as a conference paper at ICLR 2021
Recently, FSL has been dominated by meta- meta-learning based approaches (Finn et al. 2017 Snell et al. 2017 Sung et al. 2018 Lee et al., 2019; Ye et al. 2020), which exploit the ample samples from base classes via episodic training. During meta-training, to imitate an N-way K-shot novel class recognition task, an N-way K-shot episode/mettataskk is sampled in each iteration from the base classes, consisting of a support set and query set. By setting up the meta-training episodes exactly the same way as the meta-test ones (i.e., N-way K-shot in the support set), the objective is to ensure that the meta-learned model can generalize to novel tasks. However, this also leads to an unwanted side-effect, that is, the model will be susceptible to the poor sampling of the few shots.
Outlying training instances are prevalence in vision benchmarks which can be caused by various factors such as occlusions or unusual pose/lighting conditions. When trained with ample samples, modern CNN-based recognition models are typically robust against abnormal instances as long as they are not dominant. However, when as few as one shot per class is used to build classifier for FSL. the moorly-sampled few shots could be catastrophic, e-g., when the cat class is represented in the support set by a single image of a half-occluded cat viewed from behind, it would be extremely hard to build a classifier to recognize cats in the query set that are mostly full-body visible and frontal. Existing main reason is that different episodes are sampled randomly and independently. When the cat class is sampled in two episodes, these models are not aware that they are the same class, and thus cannot enforce the classifiers independently learned to be consistent to each other, regardless whether there exist poorly-sample shots in one of the two episodes.
ceisodic-training based FSL models do not offer any solution to this problem. The
In this paper, a novel meta-learning via modeling episode relationships (MELR) framework is proposed address the poor sampling problem of the support set instances in FSL. In contrast to the existing episodic training strategy. MELR conducts meta learning over two episodes deliberately sampled contain the same set of base classes but different instances. In this way, cross- cross-episode model consistency can be enforced so that the meta- meta-leermed model is robust against poorly shots in the meta-test stage. Concretely, MELR consists of two key components: Friss-Ep Attention Module (CEAM) and Cross- Consistency Regularization (CECR). CEAM is com- posed of a cross-episode transformer which allows the support set instances to be examined through attention so that unrepresentative support samples can be identified and their negative effects allevi- ated (especially for computing class prototypes/centers) CECR, on the other hand, exploits the fact that since the two episodes contain the same set of classes, the obtained classifiers (class prototypes) should produce consistent predictions regardless whether there are any the support and/or which episode a query instance comes from. This consistency is enforced via cross-episode knowledge distillation.
instances in
Our main contributions three-fold: (1) For the first time, the poor sampling problem of the few shots is formally tackled by modeling the episode-level relationships in meta-learning based FSL. (2) We propose a novel MELR model with two cross-episode components (i.c., CEAM and CECR) to explicitly enforce that the classifiers of the same classes learned from different episodes need to be consistent regardless whether there exist poorly- saalles shots. (3) Extensive experiments
for non-transductive standard FSL on two benchmarks show that our MELR achieves significant improvements over the baseline ProtoNet (Snell et al., (2077) and even outperforms the latest com- petitors under the same settings. We will release the code and models soon.
2 RELATED WORK
Few-Shot Learning. Few-shot learning (FSL) has become topical recently. Existing methods can
be generally divided into four groups: (1) Metric-based methods either learn suitable embedding
space for their chosen/proposed distance metrics (c.g.. cosine similarity (Vinyals et al. 2016), Eu-
clidean distance (Snell et al. 2017). and novel measure SEN (Nguyen et al. (0200 or directly learn a suitable distance metric (e.g.. CNN-based relation module (Sung et al. 2018 Wu et al.)
2019). ridge regression (Bertinetto et al. 2019), and graph neural networks (Satorras & Estrach
2018 Kim et al.] 2019 Yang et al. 2020)). Moreover, several approaches (Yoon et al. 2019 Li et al. 2019a| Qiao et al. 2019; Ye et al. 2020 Simon et al., 2020) learn task-specific metrics which
adaptive to each episode instead of learning shared -agnostic metric space. (2) Model-based
methods (Finn et al., 2017; Nichol et al. 2018 Rusu et al., 2019) learn good model initializations
on base classes and then quickly adapt (i.e., finetune) them novel classes with few shots and
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Published as a conference paper at ICLR 2021
limited number of gradient update steps. (3) Optimization-basen methods (Ravi & Larochelle 2017
Munkhdalai & Yu, 2017; Li et al. 2017) aim to learn to optimize, that is, to meta-learn optimiza-
tion algorithms suitable for quick finetuning from base to novel classes. (4) Hallucination-
methods (Hariharan & Girshick 2017 Wang et al. 2018 Schwartz et al. 2018 Li et al.] (020) learn generators base classes and then hallucinate new novel class data to augment the few shots. Additionally, there are also other methods that learn to predict network parameters given few novel class samples (Qiao et al. 20118] Gidaris & Komodakis 2019 Guo & Cheung 2020). Although the metric-based ProtoNet (Snell et al. 2017) used as our baseline in this paper, our proposed MELR framework can be easily integrated with other spisodic-training based methods.
Modeling Episode-Level Relationships. In the FSL area, relatively less effort has been made to explicitly model the relationships across episodes. For modeling such episode-level relationships, there are two recent examples: (1) LGM-Net (Li et al. (2019) proposes an inter-task normalization strategy, which applies batch normalization to all support samples across a batch of episodes in each training iteration. (2) Among batch of episodes, Meta-Transfer Learning (Sun et al. 2019) records the class with the lowest accuracy in each episode and then re-samples 'hard' meta-tasks from the set of recorded classes. In this work, instead of utilizing the relationships implicitly, we propose to model episode-level relationships (MELR) explicitly by focusing on episodes with the same set of classes. Furthermore, our MELR is specifically designed to cope with the poor sampling of the few shots - an objective very different from those in (Li et al. 2019b Sun et al.] 2019).
Attention Mechanism. Attention mechanism was first proposed by (Bahdanau et al. 2015 for ma- chine translation and has now achieved great success in natural language processing Vassann et al., 2017) and computer vision (Xu et al. 2015). An attention module typically takes a triplet (queries, keys, values) as input and learns interactions between queries and key-value pairs according to cer- tain task objectives. is referred to as self-attention or pross-attentin depending on whether keys and queries are the same. Several recent works (Hou et al., 2019; Guo & Cheung 2020; Ye et al., 2020j have utilized attention mechanism for meta-learning based FSI. CAN (Hou et al.) 2019) employs cross-attention between support and query samples to learn better feature representations. AWGIM (Guo & Cheung 2020) adopts both self- and cross-attention for generating classification weights. FEAT (Ye et al. 2020) only uses self-attention on the class prototypes of the support set. The biggest difference between these methods and our MELR lies in whether attention is modeled within each episode or across episodes. Only MELR allows modeling cross-episode instance at- tention explicitly so that the meta-learned model can be insensitive to badly-sampled support set instances. In addition, in our MELR, query set instances whilst existing models such as FEAT only apply attention to prototypes obtained using support set instances. They thus cannot directly handle instance-level anomalies.
also updated using cross-attention
3 METHODOLOGY
3.1 PROBLEM DEFINITION
Let Do- (1),,)))l E C, = 1,2, .N) denote an abundant meta-training set from base classes Co. where I; is the i-th image, y/ denotes the class label of v,, and N₂ is the number of denote a few-shot sample
images in D.. Similarly, let Dn = {(.,, yt) yk)lu E C.. i = 1,2, ... .N,} .N.) set from a set of novel classes C, (e.g., K-shot means that each novel class has K labeled images and N, = K(C,,)] ). where C. n nC,, = We are also given a test set T from Cn, where D, nt T = 0. By exploiting D, and D, for training, the objective of few-shot learning (FSL.) is to predict the class labels of test images in T.
3.2
META-LEARNING BASED FSL
Most FSL methods are based on meta-learning ( (Vinyals et al.) 2016 Finn et al. 2017 Snell et al.)
2017 Sung et al. 2018 Lee et al. 2019 Ye et al. 2020), which adopt episodic training on the base class sample set Dj and test their models over few-shot classification tasks sampled from the novel classes C.. Concretely, an N-way K-shot Q-query episode e = (Se, Qe) is generated as follows: (1) We first randomly sample subset C. from base classes C. during meta-training (or from novel classes C, during meta-test) and re-index it as C, = 21,2, .N.. (2) For each class in C.,, K support and Q query images are then randomly sampled form the support set S. ((x,, y:)lya
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5
5(1)
queries
min
lissc
CEAM
((:
keys, values
shared
min
Lceer
weights
CEAM
fC:
142)
Lisc
((2)
queries
S
min
The schematic illustration of the proposed MELR model. consists of two main com- ponents for modeling episode-level relationships: Cross-Episode Attention Module (CEAM) and Cross-Episode Consistency Regularization (CECR). For clarity, only the 5-way 1-shot setting presented here. Each red/blue cuboid denotes single instance.
Figure 1:
C. 1.2..... .N K} and the query set Q. (S. Q. = 0) respectively.
E C., 1,2, NN x Q}
A meta-learning based FSL approach typically designs few-shot classification loss over the query set Q. for each meta-training episode e:
Lfso(e) (G.. Ly,, ((b(x);
where v denotes a feature extractor with an output dimension d. f(: (S:) Fd R can be any scoring function constructed from the support set Se, and L(: :) is the classification loss (e.g.. widely used cross-entropy). By minimizing the above loss function via back propagation to update the part of the model to be meta- meta-learned (e.g., &b in ProtoNet), the model is trained over many meta-training episodes and then evaluated on the meta-test episodes.
3.3 MODELING EPISODE-LEVEI RELATIONSHIPS (MELR)
In the FSL area, relatively less effort has been made to explicitly model the relationships across episodes, and many FSL methods define their loss functions within each episode independently. In contrast, with our Modeling Episode-Level Relationships (MELR), two N-way episodes are sam- pled in each training iteration from exactly the same set of N base classes. Cross-Episode Attention Module (CEAM) and Cross Pross-Episode Consistency Regularization (CECR) are then devised to exploit this type of episode-level relationship explicitly (see Figure
Cross-Episode Attention Module (CEAM). We are given two N-way K-shot Q-query episodes o!²)) sampled from the same subset C₂ of C., where C2 is re-indexed as C. 21.2. NN.. and e()) (2 O. For both episodes, to minimize the negative impact of badly-sampled few shots for given query instance, we propose CEAM for cross-episode attention modeling, which is detailed below.
(!I) -S!") ( and (2)) (S!
Concretely, let (1) = (a(a)):: v E s E NRxd (or 2(2) and Q = [+b( E 2 E NNQxd (or 22) denote the feature matrices of support and query samples in ell) (or c(2)), respec- tively, and let (!)) = () Q()) E N(K+)) (or 2 = 2 Q (()) be the feature matrix of all samples in (1)) (or e(.)). For episode e(1), CEAM takes the triplet (F(1),
as input, which corresponds to the input (queries, keys, values) in typical attention module:
(1) (2)
(()) ( ((P), (S())s())) F()) softmax
Q K
(2)
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where the imputs first linearly mapped into a latent space with the same dimension of the feature space (using projection matrices WQ. K, Wv E Rdxd);
F (1) FOW We RN(K+))Xd Q
(3)
S 2) W E RRNKx
(4)
S/ = S E RNxx.
(5)
Similarly, for episode el²), we have (analogous to Eq. 2
F
p O)T
(2) = CEAM(F (2), (2) , = F softmax
( V
(6)
where the learnable parameters of fully connected layers (i.e., Wo. Wx k and Wv) are shared across Eq. ( and Eq. (6). We can then obtain the transformed support and query embedding matrices in (1) (or e(2) from f(1) = (5) (}) (or f)(2) = (2) (.
Cross-Episcn Consistency Regularization (CECR). In our MELR model, CEAM utilizes instance-level attention to alleviate the negative effects of the poor support set instance sampling so that each query set instance can be assigned to the right class with minimal loss. Our CECR designed to further reduce the model sensitivity to badly-samples shots in different episodes by forcing the classifiers learned over the episodes produce consistent predictions. There are various options on how to enforce such consistency. CECR adopts knowledge distillation based strategy empirically it is the most effective one (see Section 4.3
Let ((:S(1) Rdd RV and f(:;S(2)) Rd N be the scoring functions of the two clas- sifiers constructed from S(1) and $(2), respectively. To determine which classifier/scoring func- tion is stronger, we compute the few-shot classification accuracies of the two classifiers on the
merged query samples from both episodes. Concretely, let o! {(q MM ())) I
1,2, } (or @!²)) denote the set of transformed embedding vectors of query samples in (1))
(or el2)), where qf" (or q ((2) denotes the i-th row of the transformed embedding matrix Q(1) (or
((2)), With ( = de U o!2) {(q (1.2) y, ( = 1,2, 2NQ)), we are able to com-
pute the few-shot classification accuracies of the two classifiers w.r.t. the ground-truth labels and
corresponding predicted ones (i.c., arg max, oj (f((()) (1-2), ;(i))
and arg max, Bj (f((((())) ((2)
(j 1.2,.... NN for (1.2) y (1.21) e o!1.2) where (v) A exp(u,) softmax function).
- exp(ey) (v E TRA) is the
The classifier with higher accuracy is thus considered to be the stronger and subsequently used as the teacher classifier for the student to behave consistently with Without loss of generality, we assume that f-; (()) is stronger than f(: ((:))))). We choose the knowledge distillation loss (Hinton al. 2015) for CECR, which is stated as:
Leeor (e (2) = (a (1. w (12)) oo. (2) f(q (1.2), ((2)))
where is the temperature parameter as used in (Hinton et al. 2015). More specifically, when the softmax function (v; A eTp(e,/T)
Ey_ exp(u./T) (v R.V j 1,2, is used, we define
(12) (()), ( ((q (1.2), $2)): in Eq. with the cross-entropy loss:
L'(fQa (1.2) S()), f(g (1.2) ((1)):
N
(f
(1.2), (800)) T) log
(8)
Note that we cut off the gradients over f(: (()) when back propagating since the output of the teacher scoring function is treated as the soft target for the student.
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Published as a conference paper at ICLR 2021
Algorithm MELR-based FSL
Input: Our MELR model with the set of all parameters @
The base class sample set D3
Hyper-paramelers X, T
Output: The learned model for all iteration
... MaxIteration do
Randomly sample e(a) and e(2) from Db. satisfying that c.
c! e e2 = 0:
2:
3: Compute (())) for e using CEAM with Eq. ( and obtain f(2) with Eq. 6 similarly.
4: Compute (e)s((el)) and I ()))(e(() with Eq. (9, respectively;
Construct 2(1.2) = ②!th) U 2 based on the two episodes;
5:
6:
Determine the teacher episode el!) and the student e(+) by computing the few-shot classifica- tion accuracies of the two classifiers within e!1) and e(²), respectively;
7:
Compute the CECR loss Leeer (e(*), e(+); with Eq. (7)
8:
Compute the total loss Lestal with Eq. (10):
9:
Compute the gradients Vel
Update using stochastic gradient descent;
11: end for
return The learned model.
3.4 MELR-BASED FSL ALGORITHM
As have mentioned above, in each training iteration, we randomly sample N-way K-shot Q- query episodes e(1) (s ) and e(2) = (S!²) e.²²), which must have exactly the same set of classes but with different instances. We first transform the feature embeddings with Cross Cross-Episee -pissod Attention Module (CEAM) and then compute the few-shot classification loss adopting ProtoNet
Snell et 2017 for both episodes (e (e(1), el))))
L [ese(E) (a..ne @ L1n,, PrmmnNer $))
(.. log dz (JProcoNer (q:: S)).
Next determine the stronger/teacher episode to compute the Cross-Episode Consistency Regu- larization (CECR) loss between the two episodes. The total loss for MELR is finally given by:
Liotal - (_ L)oc(e(2)) (e (())))))
(10)
where (t) E {e(1), e2)) denotes the teacher episode, and elo) E (e(1) el2) } is the student.
By combining CEAM and CECR for episodic training, our MELR- FSL algorithm is summa- rized in Algorithm]] Once learned, with the optimal model found by our algorithm, we randomly
sample multiple N -way K-shot meta-test episodes from C, for evaluation. In other words, the episode-level relationship is only exploited during meta-trre vocta-training.
EXPERIMENTS
4.1 DATASETS AND SETTINGS
Datasets. Two widely-used benchmarks are selected: (1) minilmageNet (Vinyals et al. 2016): It
contains 100 classes from ILSVRC-12 (Russakovsky al. 2015). Each class has 600 images. We split it into 64 training classes, 16 validation classes and 20 test classes, in (Ravi & Larochelle]
2017. (2) tieredImageNet (Ren al. 2018): It is larger subset of ILSVRC-12, containing 608 classes and 779,165 images in total. We split it into 351 training classes, 97 validation classes and 160 test classes, as (Ren et al. 2018). All images of the two datasets are resized 84 x 84.
settings are used. Each test episode (test) (Sfteat) ef (test)) has classes randomly sampled from the test split, with 5 or I shots and 15 queries per class. We thus have N 5, K 5 or Q 15 in previous works. Although meta-train
Evaluation Protocols. The 5-way 5-shot/1

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Published as a conference paper at ICLR 2021
Table 1: Comparative results of standard FSL. on benchmark datasets. The average 5-way few-
shot classification accuracies (%, top-1) along with the 95% confidence intervals are reported.
mmnrilmageNe
migredl
Method
Backbone
1-shot
5-shot
1-shot
5-shot
MatchingNet |Vinyals al. 2016
Conv4-64
43.56 + 0.84
55.31 = 0.73
ProtoNet! (Snell
2017
Conv4-64
52.78 + 0.45
71.26
0.36
53.82
0.48
71.77 + 0.41
MAML (Fina al..
Conv4-64
48.70 + L.84
63.10
0.92
51.67
1.81
70.30 = 0.08
Relation [Sung
2018
Conv4-64
50.40 1 0.80
65.30 1 0.70
54.48
1 0.93
71.32 1 0.78
IMP (Allen ct 2019
Conv4-64
49.60 + 0.80
68.0
DN4 TTr al.
2019c
Conv4-64
51.24 + 0.74
7..02
PARN Wu et a.. 2019
Conv4-64
55.22 = 0.84
71.55
PN+rol Gidars el al.
2019
Conv4-64
53.63 1: 0.43
71.70
CC+rot Gidari ci al.
2019
Conv4-64
54.83 + 0.43
71886 0.33
Centroid AArrssyyaii Arman
2020
Conv4-64
53. 13.1444 1 1.06
71.45 + 0.72
Neg-Cosine (Liu ct al.
2020
Conv4-64
0.76
70.41 0.66
FEAT Yee et al. 2020
Conv4-64
55.15 +: 0.20
71.61 1 0.16
MELR (ours)
Conv4-64
55.35
1 0.43
72.27 1 0.35
56.38
0.48
73.22 1 0.41
ProtoNet (Snell et al. 2017
Conv4-512
53.52 + 0.43
73.34 + 0.36
55.52
0.48
74.07 + 0.40
MAML (Finn al. 2017
Conv4-512
49.33 = 0.60
65.17=0...
52.84
+ 0.56
70.91
= 0.46
Relation Net (Sung al.
2018
Conv4-512
50.86 0.57
67.32 1 0.44
54.69
0.59
72.71 1 0.43
PN+rot GGdans el a. 2019
Conv4-512
56.02 : 0.46
74.00 0.35
CC+rol Gidans al.
2019
Conv4-512
56.27 + 0.43
74.30
0.33
MELR (ours)
Conv4-512
57.54
0.44
74.37
1 0.34
60.26
0.51
77. 1 0.40
ProtoNet (Snell
2017
ResNet-12
62.41 + 0.44
80.49
0.29
69.63
0.53
84.82 + 0.36
TADAM (Oreshkin
2018
ResNet-12
58.50 + 0.30
76.70 0.38
MetaOptNet (Lee
2019
R
62.64 1 0.61
0.46 78.63
65.99 0.72
81.56 1 0.63
MTL (Sun et ät..
2019
ResNet-12
61.20 + 1.80
0.80
65.62 + 1.80
80.61 = 0.90
AM3 (Xing et al. 2019
ResNet-12
65.21 + 0.49
75.20+0.33
67.23 + 0.34
78.95 + 0.22
Shot-Pree (Kuvichundrun et ul.
2019
ResNet-12
59.04 0.43
77.64
66.87 0.43
82.64 = 0.43
Neg- Bog-Cosine (Liu el al. 2020
Re
63.85
0.81
81.57 1 0.56
Distill (Tian et al. 2020
ResNet-12
64.82 0.60
0.44 82.14 0.43
71.52 1.66
86.03 1 0.49
DSN-MK (Simon ci al. 2020
ResNet-12
64.60 0.72
79.51 + 0.50
67.39
0.82
+ 0.82
82.85 + 0.56
DecpEMD' (Zhang ci al.
2020
ResNet-12
65.91 + 0.82
82.41 0,56
71.16 0.87
$6.03 = 0.58
FEAT (Ye al. 2020
ResNet-11
66.78 1 0.20
82.05
1 0.14
70.80
1 0.23
84.79 1 0.16
MELR (ours)
ResNet-12
67.40 + 0.43
83.40 + 0.28
72.14 0.51
87.01 + 0.35
our MELR with two episodes in each training iteration, we still evaluate over test episodes one by one, strictly following the standard setting. Moreover, since cross-episode relationships can be used when testing, we take ( (test)) (test)) as the input of CEAM. Note that the meta-test process is trn-trrasductive since the embedding of each query sample in ((eest) is independently up- dated using the keys and values coming from the support set. We report average 5-way classification accuracy (%, I) over 2,000 test episodes as well as the 95% confidence interval.
Implementation Details. Our MELR algorithm adopts Conv4-64 (Vinyals et al. 2016). Conv4- 512 and ResNet-12 (He et al. 2016b) as the feature extractors s/s for fair comparison with published results. The output feature dimensions of Conv4-64, Conv4-512 and ResNet-12 are 64, 512, and 640, respectively. To accelerate the entire training process, we pre-train all three backbones on the training split of each dataset as in many previous works (Zhang et al. 2020 Ye et al. 2020) Simon et al. 2020). We use data augmentation during pre- pre-training (as well as meta-training with ResNet- 12 on minilmageNett. For ResNet- 12, the stochastic gradient descent (SGD) optimizer is employed with the initial learning rate of 1e-4, the weight decay of 5e-4, and the Nesterov momen- tum of 0.9. For Conv4-64 and Conv4-512, the Adam optimizer (Kingma & Ba 2013) is adopted with the initial learning rate of 1e-4. The syper-paramettrs À and T' are respectively selected from {0.02, 0.05, 1, 0.2) and { 16, 32, 64, 128} according to the validation performances of our MELR algorithm (see Appendix A.5 for more details). The code and models will be released soon.
4.2 MAIN RESULTS
We compare our MELR with the representative/ssatt--ff -frt methods for standard FSL on the two benchmark datasets in Table Note that we re-implement our baseline (i.c., ProtoNet, denoted with !) by sampling two episodes in each training iteration since it is still considered as a strong FSL approach especially when the backbone is deep. We can observe from Table I that: (1) Meth-
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Figure 2: (a) Ablative results for our MELR. (b) Comparison among different ProtoNet'+CEAt alternatives. (c) Comparison among different ProtoNet' +CECR alternatives. All results are obtained under the 5-way 1-shot and 5-shot settings minilmageNet with Conv4-64 as the feature extractor.
ods trained with ResNet-12 generally perform better than those employing shallower backbones. Also, methods trained with Conv4-512 generally perform better than those employing Conv4-64 though the backbones of the same depth. This expected because deeper and wider backbones have better representation learning abilities. (2) Our MELR achieves the new state-of- the-art performance on both benchmarks under all settings. Particularly, the improvements over the baseline (i.e., ProtoNet') range from 1.0% to 5.0%, which clearly validates the effectiveness and the strong generalization ability of our MELR. (3) On both benchmarks, the improvements obtained by our MELR over ProtoNet under the 1-shot setting are significantly larger than those under the 5-shot setting. This demonstrates the superior performance of our MELR for FSL with less shots. Again this is expected: FSL with less shots is more likely to suffer from the poor sampling of the few support instances; such a challenging problem is exactly what our MELR is designed for.
4.3 FURTHER EVALUATION
Ablation Study. To demonstrate the contributions of each cross-episode learning objective in our MELR, we conduct experiments on minilmageNeet by adding these learning objectives the base- line (one at time) under the 5-way 1-shot and 5-shot settings. Note that ProtoNet without is trained with one episode in each training iteration while ProtoNet trained with two episodes per iteration. The ablation study results in Figure 2(a] show that: (1) Increasing mini-batch size helps little for ProtoNet, indicating that our MELR benefits from two cross-episode objectives rather than doubling the mini-batch size. (2) CEAM or CECR alone clearly improves the performance of the bascline model and CEAM appears to be more beneficial to FSL than CECR. (3) The combination of the two cross-episode learning objectives in our full model (i.e., MELR) achieves further improve- ments, suggesting that these two learning objectives are complementary to each other. Moreover, in Appendix [A.2] conduct more ablative experiments when the attention module is applied within each episode, validating the necessity of cross-episode attention.
Comparison CEAM Alternatives. As have described, CEAM takes the support samples as 'keys' and 'values', and all samples in one episode as 'queries' for attention module (denoted as Alll, We can also input prototypes (mean representations support samples from the same class) as 'keys' and 'values' or input only query samples as 'queries' for attention. This re- sults in other three alternatives of CEAM: Prototype-Quurry. Support-+Query. and Prototype-+Al. Note that under the 5-way 1-shot setting, Prototype-Query is equal to Support-<Quer and Prototype-+All is the same as Support
Additionally, we compare All-AAll: inputting all samples from the other episode "keys' and 'values" for CEAM when training but still testing as Support->Al (not violating the non-transductive setting). From the comparative results of different choices in Figure 2bb) works worse than both Prototype->All and Support->All. One possible explanation that All-All exploits all query set instances during meta-training but only has access the support set during meta-test to conform to the inductive learning setting. This mis-match reduces its effectiveness.
that Support-AAl is the best for CEAM. Moreover, All-AAl
Comparison to CECR Alternatives. Our consistency regularization loss Leeer in Eq. is de- fined with the knowledge distillation (KD) loss, which can be easily replaced by the negative cosine similarity (NegCos), symmetric Kullback-Leible divergence (symKL.), the 1.2 distance (see Ap-
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(b)
(d)
Figure 3: (a) (c) Visualizations of data distributions obtained by ProtoNet', PCOAONer++EEAM, and ProtoNet' +CEAM+CECR (i.e., our MELR) for the same meta-test episode, respectively. (d) (e) Visualizations of attention maps for query and support sets, respectively. All results are obtained under the 5-way 5-shot setting on minilmageNet with Conv4-64 as the feature extractor.
pendix A.1 for more details). The results obtained by ProtoNet++CECH using different consistency losses are shown in Figure 2(c) It can be seen that ProtoNet\+KD performs slightly better than other implementations. We thus choose KD as our CECR loss.
Visualizations of Data Distributions and Attention Maps. MELR is designed to alleviate the negative effects poorly-sampled few shots. To validate this, we further provide some visual- ization results in Figure 3 (1) We sample episode in the test split of minilmageNet under the 5-way 5-shot setting and obtain the embeddings all images using the trained models of ProtoNet',
PootoNet+CEAAN and our MELR (i.e., ProtoNef+CEEM+CCCC +CEAM+CECR) respectively. We then apply
SNE to project these embeddings into a 2-dimensional space in Figure 3(a) subfigures, samples with the same color belong to the same class and diamonds are class proto- types/centers. We can observe that adding CEAM makes the distribution of different classes more separable (see Figure 3(b) vs. 3(a),, validating the effectiveness of our CEAM. Moreover, the embed- dings obtained by our MELR are clearly much more evenly distributed with the prototypes right in the center, indicating less outlying instances (see Figure 3(c) vs. 3(b). This shows that when CECR is combined with CEAM, those badly-sampled shots in Figure 3(a) are now pulled back to the center of the class distributions. our trained MELR. Since we take all samples in the episode as 'queries', and support samples as 'keys' and 'values' for CEAM when metaatestingg each of 100 samples has 25-dimensional weight vector under the 5-way 5-shot setting. For each weight vector, average the weights of the same class and obtain a 5-dimensional vector. For 75 query samples, we average the vectors of samples ob-
3(c Across three
(2) We also visualize the attention maps over the meta-test episode using
5 instance attention map (see Figure 3(d)) Similarly, tain the attention map for 25 support samples (see Figure 3(e)). It can be seen that the two attention maps are very much alike, indicating that support and query sample embeddings are transformed by our CEAM in a similar way, which thus brings performance improvements for FSL.
with the same class, resulting in
CONCLUSION
We have investigated the challenging problem of how to counter the negative effects of badly- sampled few shots for FSL. For the first time, propose to exploit the underlying relationships be- tween training episodes with identical sets of classes explicitly for meta-learning. This achieved by two key components: CEAM is designed for neutralizing unrepresentative support set instances, and CECR is to enforce the prediction consistency of few-shot classifiers obtained in the two episodes. Extensive experiments for con-transductive standard FSL on two benchmarks show that MELR achieves 1. 1.0%-5.00 improvements over the baseline (i.e., ProtoNet) used for FSL in our model and outperforms the latest competitors under the same settings.
ACKNOWLEDGMENTTS
This work was supported in part by National Natural Science Foundation of China (61976220 and 61832017). Beijing Outstanding Young Scientist Program (BJJWZYJHO122 T1TH0HH01010000009900 Open Project Program Foundation of Key Laboratory of Opto-Electronics Information Processing, Chi- nese Academy of Sciences (0EIP-0-202006), and Alibaba Innovative Research (AIR) Program.

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A APPENDIX
A.1 DETAILS ABOUT CECR ALTERNATIVES
For the cross-episode consistency regularization (CECR) loss Teeer in Eq. 77, we compare the knowledge distillation (KD) loss to the negative cosine similarity (NegCos), the L2 distance, and the symmetric Kullback-Leibler (KL) divergence (SymKL) in the main paper. Here give the details about three CECR alternatives. Concretely, let
(q) (1))) (or (q denote the normalized
vector of f9: ( (()) (or f(g (1.2), ((2)) using softmax, then have
(1.2),
L (NegCoo)) = (q (. vt be on..) NegCos(f(A)) (1.2) , f(q. (122) 2) cear
< (.2) (2) (9
-R (q ( J0 (a
(11)
| (q) )1
L
- E (. m 1 L2 (g.
(12)) (())) f( (.2). ((2)))
- E (a m. w
( Je
(22) (q ||ا
(12)
L (3ymKKL)
-E (q. a. sh ( e
(.2) ( f(q (12) (
ceer
-KL(f() 21 (()), f(q (()))/)
+ +KL(S(9 (12) ( l(q (1.2) (0))))
(13)
where <, > is the inner product of two vectors, T is the temperature parameter, and KL(u, v) Ej=1 T7 (u) log g:(u)
= n;(v) (u, N are two unnormalized scoring vectors, a denotes the softmax function, and o, (u) denotes the j-th element o())).
A.2 MORE ABLATIVE RESULTS
In Table we show more ablative results when the attention module is applied within each episode independently (named as Episod Attention Module (IEAM)). Concretely, IEAM is used for ProtoNet+IEAAN (" means that ProtoNet is trained with two episodes in each training iteration for fair comparison) as follows (i = 2):
b OT
( = IEAM(F ( ( S())) = F($) + softmax
S
(14)
Vd
We also add our Cross-Episode Consistency Regularization (CECR) for to see the performance when IEAM instead our CEAM is adopted.
We can see from Table 2 that adding IEAM to the baseline ProtoNet! also improves its performance, IEAM not as beneficial as our CEAM (see ProtoNet P +IEAM vs. ProtoNer*+CEAM +CEAM), When CECR is applied top of ProtoNet +IEAM (i.e., ProtoNet TrotoNeet+HEAMMCEEC +IEAM+CECR), the improvement +IeeoNeet+IEMMMMM
rather minor under the 5-way 1-shot setting and the result even gets worse under the 5-shot setting. However, our MELR still benefit from CECR (see MELR vs. ProtoNet ProtoNet +CEAM), indicating that CECR is not suitable for IEAM and our CEAM is necessary.
Table 2: Comparative results when IEAM is used on minilmageNet. The average 5-way few-shot classification accuracies (%, top-1) along with the 95% confidence intervals are reported.
minilmageNet
Method
Backbone
1-shot
5-shot
ProtoNet" (Snell et 2017)
Conv4-64
52.61 + 0.42
71.33 + 0.36
ProtoNet +IMeNeet+IEAMMM
Conv4-64
54.83 0.43
71.81 = 0.35
ProtoNet ProtoNeti+IEAM-CEE +EAM+CECR
Conv4-64
54.97 0.44
71.72 + 0.35
ProtoNer PeRtoNe+*+EEMMM +CEAM (ours)
Conv4-64
55.01 + 0.43
72.01 + 0.35
MELR (ours)
Conv4-64
55.35 0.43
72.27 + 0.35
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Figure 4: The first three subfigures in each row are the visualizations of data distributions obtained by ProtoNet', ProtoNet's +CEAM, and "COOONN" (i.c., our MELR) for the same meta- test episode, respectively. The last two subfigures in each row are the visualizations of attention maps Published as a conference paper at ICLR 2021
for query and support sets, respectively. All results are obtained under the 5-way 5-shot setting on minlmageNet with Conv4-64 as the feature extractor.
A.3 MORE VISUALIZATION RESULTS
we provide more visualization results in Figure (1) We sample five episodes (corresponding five rows in Figure in the test split of minilmageNet under the 5-way 5-shot setting and visualize the data distributions in the first three columns using the trained models of ProtoNett, PrEMM and our MELR (i.e.. ProtoNet +CEAM+CECR), respectively. We can observe that adding CEAM makes the distributions of different classes more separable in the first four rows (see the second column vs. the first column), validating the effectiveness of our CEAM. Moreover, the embeddings obtained by our MELR are clearly much more evenly distributed with the prototypes generally right in the center, indicating less poorly-sampled instances (see the third column vs. the second column). Specifically, ProtoNer' +CEAM brings an obvious outlying instance in the last row, but adding CECR stabilizes the training of CEAM. word, when CECR is combined with CEAM, the badly-sampled shots can be pulled back the center of the class distributions. (2) We also visualize the attention maps over each meta-test episode using our trained MELR R model. For each of the five episodes, obtain two 5 5 attention maps for query and
Similar to Section 4.3
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Table 3: Comparative results of fine-grained FSL on CUB. The average 5-way few-shot classifica- tion accuracies (%, top-1) along with the 95% confidence intervals are reported.
CUB
Method
Backbone
1-shot
5-shot
MatchingNet (Vinyals et 2016)
Conv4-64
61.16 1 0.89
72.86 = 0.70
ProtoNet (Snell et al. 2017
Conv4-64
64.42 + 0.48
81.82 = 0.35
MAML (Finn et al. 2017)
Conv4-64
55.92 - 0.95
72.09 = 0.76
Relation Net (Sung
2018
Conv4-64
62.45 0.98
76.11 + 0.69
FEAT (Ye et al.] 2020)
Conv4-64
68.87 + 0.22
82.90 + 0.15
ProtoNet +CEAM (ours)
Conv4-64
68.92 0.50
84.54 + 0.32
MELR (ours)
Conv4-64
70.26 + 0.50
85.01 + 0.32
75-
75-
70
1-shot, T= 128
65-
1-shot, -0.1
-0005
3.2
0.15
92 16
128
Figure 5: Illustration of hyper-parameter analysis the minilmageNet dataset. Conv4-64 is used as the feature extractor.
support sets in the last two subfigures of each row, respectively. It can be seen that the two attention maps each row are very much alike, indicating that support and query sample embeddings are transformed by our CEAM in a similar way, which thus brings performance improvements for FSL.
A.4 RESULTS FOR FINE-GRAINED FSL.
To evaluate our MELR under the fine anne-griined setting where the voorly-samplee shots may have greater negative impact since the classes much more close, conduct experiments on CUB- 200-2011 1 Birds (CUB) (Wah et al. 2011) with Conv4-64 as the feature extractor. CUB has 200 fine- grained classes of birds and 11,788 images in total. We follow (Ye et al.) 2020) and split the dataset into 100, 50, and 50 classes for training, validation, and test, respectively. For direct comparison, also use the pre-trained backbone model released by (Ye al. 2020), which is pre-trained on the training set. The comparative results in Table show that: (1) Our MELR achieves the best results and improves over the second-best FEAT by I.4% 2.1%. validating the effectiveness of MELR under the fine- grained setting.
(2) Our ProtoNet +CEAM alone outperforms all the competitors, and adding CECR into ProtoNet +CEA (i.e., our MELR) further brings noticeable improvements (0.5% 1.3%), indicating that both CEAM and CECR are crucial for fine-grained FSL..
A.5 ANALYSIS OF PAPEERPARRMMTER SENSITIVITY
As we have mentioned in Section the {0.02, 0.05, 0.1, 0.2) and {16, 32, 64. 128} according to the validation performance of our MELR algorithm. Concretely, on minilmageNet (with Conv4-64 64 as the feature extractor), we choose À = 0.1 and T 128 under the 5-way 1-shot setting, and choose X 0.05 and T 64 under the 5-way 5-shot setting. In Figure 5 we further present our hyper- parameter analysis on minilmageNet. The results show that our algorithm is quite insensitive to these parameters.
and T are respectively selected from
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Table 4: Results obtained by varying the number of episodes in each training iteration minilmageNet (with Conv4-64 as the backbone). The average 5-way classification accuracies (%, top-1) along with the 95% confidence intervals are reported.
Implement. (1)
Implement. (2)
Method
# Episodes
1-shot
5-shot
1-shot
5-shot
MELR
2
55.35 + 0.43
72.27 + 0.35
55.35 + 0.43
72.27 + 0.35
MELR
3
55.26
0.44
71.88 + 0.35
55.19 0.43
71.90 + 0.35
MELR
4
55.15 0.43
71.63 + 0.35
55.03 + 0.44
71.76 + 0.35
Table 5: Comparison regarding the number of parameters ('K' denotes 'x103* and 'M' denotes x 106)) among various FSL methods.
minilmageNer
Method
Backbone
Parameters
1-shot
5-shot
ProtoNet (Snell et al.
2017)
Conv4-64
113.09K
52.78 + 0.45
71.26 - 0.36
PARN (Wu al. 2019
Conv4-64
405.49K
71.55 = 0.66
FEAT Ye et al. 2020)
Conv4-64
129.66K
55.15
71.61 =
MELR (ours)
Conv4-64
129.66K
55.35 0.43
72.27 0.35
ProtoNet (Snell et al.
2017
ResNet-12
12.42M
0.44
80.49 = 0.29
FEAT Ye et al. 2020
ResNet-12
14.06M
66.78 + 0.20
82.05 + 0.14
MELR (ours)
ResNet-12
14.06M
67.40 + 0.43
83.40 + 0.28
A.6 RESULTS BY VARYING THE NUMBER OF EPISODES
We also conduct experiments by varying the number of episodes No in each training iteration. For the implementation of CEAM, we have two slightly different choices. Concretely, for each episode e(t) (i ===1 1, Ne), the output of CEAM can be defined as:
Implement. (1): (
Σ N j=1," Neriti (S
CEAM(F ( s(() (());))));
(15)
Implement. (2): 700 CEAM(F()) (SO)
(16)
where 35 j-1.ji E - the concatenation of ( E NKxd x (j 1, 1,
1, Ne). As for CECR. we determine the episode with the best accuracy as the teacher and distill
knowledge to the rest N. student episodes.
The results on minilmageNet using Conv4-64 in 4how that the performance drops slightly as the number of episodes in each training iteration increases for both implementations. One possible explanation that too much training data make the model fit better on the training set but fail to improve its generalization ability novel classes.
A.7 COMPARISON REGARDING THE NUMBER OF PARAMETERS
We select several representative/latest FSL models from Table J of the main paper and list their numbers of parameters in Table We can observe that: (1) With an extra CEAM addition the backbone (Conv4-64 or ResNet-12), our MELR has about 13% 15% relatively more parameters than the baseline ProtoNet. Note that CECR (in our MELR) leads to no extra parameters. Consid- ering the (statistically) significant improvements achieved by MELR over ProtoNet, we think that our MELR is cost-effective because requires not much additional parameters. (2) The number of MELR's parameters is almost the same as that of FEAT's and is much less than that of PARN's s, but our MELR achieves better results than FEAT and PARN, indicating that our MELR is the most cost-effective among these three methods.
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S
5(2)
8
& = CEAM S(2), (()) = WS x + WS(1)
Figure 6: Schematic illustration of our CEAM with pair of episodes as its input. For easy un- derstanding (but without loss of generality), a toy visual example considered: only one outying instance x exists in the support set ((1) of the first episode, but the support set 8(2) of the second episode is properly sampled. Since =(1)= { and 8 have similar data distributions (from the same classes), the outlying instance x pulled back S()) by attending on it with S(2) (which not be done by attending on it with (()),, i.e., its negative effect is mitigated by our CEAM.
Table 6: Results by inputting only support samples as 'queries' into CEAM (denote as 'Sup- port + Support') on minilmageNet.
Method
Backbone
1-shot
5-shot
ProtoNet
Conv4-64
52.61 + 0.42
71.33
0.36
ProtoNet (Support- + Support)
Conv4-64
54.79 + 0.43
71.40 + 0.36
ProtoNet+CCEAM (Support + All, ours)
Conv4-64
55.01 + 0.43
72.01 + 0.35
A.8 SCHEMATIC ILLUSTRATION OF CEAM
To demonstrate how our proposed CEAM can alleviate the negative effect of the poorly sampled shots, present schematic illustration of CEAM with pair of episodes its input in Figure 6 For easy understanding (but without loss of generality), toy visual example is considered: only one outlying instance x exists in the support set S(1) of the first episode, but support set S(2) of the second episode is properly sampled. Since the two episodes
sampled from the same set of classes, the data distributions of S(1) (() \ {z} and ((2) are similar. On one hand, when ((1) (including the outlier .r) is used as keys and values to update S(2), the distribution of ((2) will not be influenced too much by the outlier x since all the shots in ((2) are far away from æ and the weights on will be very small. That is, our CEAM is insensitive to few outliers in the keys and values. On the other hand, when ((1) is transformed based on 5(2), the distribution of Š(1) will be changed little (the data distributions of ((1) and S(2) are similar). Particularly, for the outlier its updated embedding & will be pulled back to $(1) (i.e., its negative effect is mitigated) since & CEAM(X, ((2),s(()) x x + WS(2) & x + WS(1,, where is the original embedding of x, ((2) and 5() are respectively the feature matrices of S(2) and $(1), W and W are two normalized weight matrices. Note that this cannot be done by attending x with ((1)
Additionally, we provide the results obtained by an extra CEAM alternative in Table 6 only the embeddings of support samples are transformed (denoted 'Support - Support') instead of trans- forming all samples as our choice. We can see from Table/]land also Figure Figure[(b)ttaan our choice (i.e., Support + All) achieves the best results among all CEAM alternatives. One possible explanation for why we resort to updating all samples is that transforming support and query samples into the same embedding space is beneficial to the model learning.
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Published as a conference paper at ICLR 2021
ACCERN 2e.00
ALOROOE EEN
72.00%
2000
$3.20%
65.0%
72.0%
Figure 7: Visualization results of 10 meta-test episodes on minilmageNeet under the 5-way 5-shot setting (Conv4-64 66 is used as the backbone). For each meta-test episode, visualize three data distributions (from left to right) obtained by ProtoNet ProtoNet +CEAM, TrCEA,, and our MELR, respec- tively. That is, each meta-test episode is denoted by three subfigures and each row has two episodes. In each subfigure, we compute the test accuracy over query samples and present it as the title.
A.9 SIGNIFICANCE ANALYSIS OF CECR
Since CECR (in our MELR) requires no extra learnable parameters and only computes a loss for consistency constraint, it brings very limited computational cost. Empirically, the meta-training time of MELR is almost the same as that ProtoNet +CEAM, indicating that the performance +CEAM improvement over PCEAM is obtained by CECR at extremely low cost.
To study when CECR has a significant impact on the final FSL performance, we select 10 meta-test episodes from the 2,000 ones used in the evaluation stage and visualize them in Figure 7 Con- cretely, for each of the 10 selected meta-test episodes. we visualize three data distributions (from left to right) obtained by ProtoNet", ProtoNet +CEAM, and our MELR, respectively. That is, each meta-test episode is denoted by group of three subfigures. In each subfigure, we compute the test accuracy over query samples and present
the title. We can that: (1) When the few-shot clas- sification task is hard (i.e., ProtoNet obtains relatively low accuracy), CEAM leads to significant improvements (about 3% 9%). (2) In the same hard situation, CECR further achieves significant improvements (about 5% 8%) on top of CEAM and shows its great effect on the final FSL perfor- mance. This indicates that CECR and CEAM are complementary to each other in hard situations, and thus both are crucial for solving the poor sampling problem in meta-learning based FSL.
A.10 RESULTS OF TRANSDUCTIVE FSL
The main difference between standard and transductive FSL is whether query samples are tested one at time all simultancously. As we have mentioned in Section 4.1 evaluate our MELR model strictly following the con-transductive setting for standard FSL since the embedding of each query sample in the test episode is independently transformed using the keys and values coming from the
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Table 7: Comparative results of transductive FSL on minilmageNet. The average 5-way few-shot classification accuracies (%, top-1) along with the 95% confidence intervals are reported. We cite the results of the competitors from Ye et 2020p.
minilmageNet
Method
Backbone
1-shot
5-shot
Standard FSL:
ProtoNet! (Snell et al.
2017
Conv4-64
52.78 + 0.45
71.26 = 0.36 72.27 = 0.35
MELR (ours)
Conv4-64
55.35 + 0.43
Transductive FSL:
Ren et al.
2018
Conv4-64
55.50 + 0.10
71.76 = 0.08
TPN (Liu et 2019
Conv4-64
55.51 0.84
69.86 = 0.67
TEAM (Qiao et al. 2019
Conv4-64
56.57 57.04 0.16
72.04 72.89 = 0.20
FEAT
YYe al. 2020)
Conv4-64
ProtoNet!+ClA (ours)
Conv4-64
60.30 + 0.49
74.28 + 0.36
MELR (ours)
Conv4-64
61.67 + 0.51
74.87 + 0.35
17
NELR
METR
Epach
Spach
Figure 8: Visualization of the generalization ability of our MELR the test split of minelmageNet under the 5-way 1-shot setting with Conv4-64 as the backbone. Note that we check the test perfor- mance of the model at each training epoch (i.e., every 100 training iterations).
support set. However, in this section, further conduct experiments under the transductive FSL setting to study how well our MELR can make use of the unlabeled query samples.
Concretely, for each meta-test episode ((test), we input all samples (both support and query ones) as keys and values into the trained CEAM:
(test) CEAM(F((tat) pp(tst)) p(test)),
(17)
such that the relationships of all unlabeled query samples can be taken into consideration. With the transformed embeddings, we make predictions for all query samples based ctt al. 2018), which utilizes the unlabeled query samples to help construct better class prototypes and then makes predictions similar ProtoNet. To match the meta-test process, we also make changes to meta-training accordingly. Specifically, for one episode (out of the two) in each training iteration, we use all samples from the other episode as keys and values for CEAM to update all of its own embeddings. This is followed by obtaining the prototypes based on Semi-ProtoNet as well as computing the FSL loss and CECR loss.
(Ren
The results of transductive FSL on minilmageNee are shown in Table 7 It can be seen that: (1) By utilizing the unlabeled query samples under transductive FSL, our MELR achieves further im- provements, as compared to MELR under standard FSL. Particularly, the performance improvement under 1-shot (i.e., 6.3%) more significant than that under 5-shot (i.e., 2.6%), indicating that ex- ploiting unlabeled query samples brings more benefits to FSL with less labeled support samples.
(2) Our MELR achieves the best results among all the transductive FSL methods. Specifically, MELR outperforms FEAT by large margin (2.0% - 4.6%).. Since FEAT also makes predictions based on Semi-ProtoNee.. this clearly validates the effectiveness our MELR under the transductive setting. (3) Our ProtoNeti+CEAM
alone outperforms all the competitors, and adding CECR into PottoNet++CEAN (i.e., our MELR is obtained) further brings noticeable improvements (0.6% 1.4%), indicating that both CEAM and CECR play important roles under transductive FSL..
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A.11 VISUALIZATIONS OF THE GENERALIZATION ABILITY OF MELR
We further provide the visualization of the generalization ability of our MELR during meta-test in Figure Concretely, randomly sample 1,000 episode pairs from the test split of miniImageNet under the 5-way 1-shot setting, where the two episodes in each pair have identical sets of classes. We then compute the average 5-way classification accuracy over all 2,000 episodes (from the 1,000 episode pairs) and the average Leser in Eq. 7 over all 1,000 episode pairs at each training epoch. We present the visualization results w.r.t. accuracy and CECR loss in Figure 8 expected, the accuracy of our MELR is consistently higher than that of our baseline ProtoNet'. Moreover, as com- pared with ProtoNet the CECR loss of our MELR is also lower across the whole training process, indicating that MELR has better performance consistency between two episodes. This provides di- rect evidence that our CEAM and CECR can boost the generalization ability of the learned model on novel classes.
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Reviews:
[{"review_id": "nnkihBavup", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary of Paper:\nThis paper proposes to improve Prototypical Networks by a method MELR which aims to fix the problem caused by poorly represented classes in sampled episodes and to reap benefits from enforcing cross-episode consistency. The proposed method achieves State of Art results on commonly used benchmarks miniImageNet and tieredImageNet. \n\nReasons for score:\nOverall, I tend towards rejecting this paper. I think cross-episode relationship is an important topic of study in the context of few-shot learning, and the experimental results in this paper are strong. However, I am unconvinced that this ‘hammer’ is the right tool for the ‘nail’ that the authors claim to solve. A more thorough study of the motivating problem and how & why MELR works would greatly improve this paper.\n\nPros:\n1.The proposed method performs well in standard benchmark datasets miniImageNet, tieredImageNet, and CUB200. Assuming correctness of experimental protocol, the improvement over previous methods is significant.\n2. Visualization of embedding space by t-SNE lends further credibility to the strong performance. Samples from each class are well clustered yet still disperse.\n3. Ablation of hyperparameters and algorithmic alternatives is mostly complete and honestly presented.\n\nCons:\n1. The paper is motivated by the supposed “poorly sampled episodes” problem. While it intuitively makes sense that some data points are more representative than others, whether this has a disparate impact on episode few-shot learning is unclear. In my opinion, the small sample problem in few-shot episodes is no worse than that encountered in standard batch training. In standard supervised learning tasks, batch size as small as 1 has been used successfully to train deep networks given sufficient training epochs. Without empirical or theoretical illustration, I am not convinced that the problem the authors seek to address is a real problem.\n2. The reasoning behind equation 2 is unclear. It is not clear why the attention module output in CEAM is added to the embedding F if the goal is to ignore bad examples and emphasize good examples. The choice to ignore class labels when doing attention is also surprising as it doesn’t use the fact that both episodes have the same classes.\n3. The proposed method aims to use inter-episode information to stabilize representation learning, hence samples two episodes at a time and apply CEAM and CECR to the joint episode. I don’t see why the authors restrict the method to just two episodes. Classifier consistency should hold transductively across any number of episodes with the same base classes. Thus, you could make the number of episodes an hyperparameter and experimentally verify what is the best choice.\n4. Grammar mistakes are common and writing generally lacks polish. \n\nQuestions:\n\tPlease address the points in cons above.\n\nMinor points and additional feedback:\nIntroduction\n“even may be impossible” -> may even be impossible\n“reliance of deep neural networks on sufficient annotated training data”: you always need “sufficient” data, FSL aims to make fewer data be sufficient.\n“Meta-training”, “episode” and “base class” used before definition\n“Concretely, MELR consists of two key components: a Cross-Episode Attention Module (CEAM) and a Cross-Episode Consistency Regularization (CECR)” -> remove ‘a’\n“two cross-episode components (i.e., CEAM and CECR) to explicitly enforcing” -> to explicitly enforce\nRelated Work\nVery few model-based methods are mentioned but I guess that’s beyond the point here.\n“Almost all existing meta-learning based FSL methods ignore the relationships across episodes” -> “Ignore” is probably not true since many works (incl. MAML) frame the problem of Meta-learning as an inter-task learning process (as presented in this review https://arxiv.org/pdf/2004.05439.pdf). There’s also this work (https://arxiv.org/abs/1909.11722) that looks at the role of shots when building episodes during and after meta-training.\nMethodology\nToo many inline equations. Even with a dedicated definition section there is still a new definition almost every paragraph. Important equations should be made standalone, definitions placed into its own section, and fluff math be removed.\nWhy does CEAM take S as argument twice? Should they be S_k and S_v instead?\nEqn 2: Is the softmax taken over rows or columns (or both) of F_qS_k^T?\n\n[Post rebuttal] I have increased the score of my review to 7. Below is a copy-pasta of my comments post discussion:\n\nWhile my original concern about how much sampling affects FSL is still not fully addressed, I think it is not a trivial question to answer and a full exploration of the topic could constitute its own paper. So although I'm not fully convinced about the motivation of this paper, I think the thorough experimental evaluation along with the strong empirical results together warrants publication. From my perspective, a particular important strength of this paper is its ablations. I'm fairly convinced that the presented implementation is likely the best way to implement the idea of cross-episode attention + distillation. I think the baseline proposed by the AC makes sense. It would be great if that could be incorporated into the final version of the paper. A possible explanation for the drop in performance when using 3 or more episodes is due to the relative decrease in episode diversity. Results on wider datasets could corroborate this hypothesis.", "extra_scores": {}, "extra_text": {}}, {"review_id": "gw7f_NOqe6a", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary:\n- This paper proposes a meta-learning method (MELR) to alleviate the negative impact of poor sampling of support sets.\nMLER consists of two main modules. The first module (CEAM) applies the attention mechanism to two sampled episodes and it alleviates the negative impact of badly-sampled instances.\nThe second module (CECR) enhances the consistency of classifiers obtained by using two episodes to deal with the sensitivity for the badly-sampled instances.\nSpecifically, to realize this, CECR utilizes a knowledge distillation framework. Experiments with two real-world datasets demonstrate that MELR works well.\n\nPros:\n- This paper proposes a new meta-learning method to alleviate the negative effect of poor sampling of support sets.\n- Experimental results show that MELR can improve the baseline method (Propnet).  These results show some evidence of the effectiveness of two proposed modules (CEAM and CECR).\n\nCons:\n- It is not clear why CEAM can alleviate the negative effect of badly-sampled support instances.\n- Hyper-parameter candidates used for MELR are not described.\n\nDetailed comments and questions:\n- Although empirical results seem to support the effectiveness of CEAM, I do not understand why CEAM alleviates the negative effect of badly-sampled few shots for a given query instance. Can the authors qualitatively explain the reason for this?\n- In the last row of fig 4, it seems that CEAM creates obvious outlier instances. Why does this happen?\n- How much does the proposed method depend on hyperparameters such as $T$ and $\\lambda$?\n\nMinor comments:\n- In eqs. (1) and (9), although argmax is taken in the loss function $L$, it is not correct when using the cross-entropy loss.\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "ZbQoHiLYmoG", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 7, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary\n\nOne problem of few-shot episodic learning is a poor sampling resulting in negative impacts on the learned model. \nThe paper proposes a new episodic training by exploiting inter-episode relationships to deal with poor sampling problem and improve the learned model by enforcing consistency regularization. Cross Episode Attention Module (CEAM) is proposed to alleviate the effect of poorly-sampled shots and Cross-Episode Consistency Regularization (CECR) is proposed to enforce robustness of the classifiers.\n\nStrength\n\n- The paper proposes a novel idea of how to improve few-shot learning by exploiting inter-episode relationships. Using multiple episodes and exploiting inter-episode is a new attempt.\n- There have been attempts to improve few-shot training by batch construction, but the proposed method outperforms the previous approaches with a sizable margin.\n- The extensive ablative studies provide comprehensive comparisons among possible design choices. (including supplementary materials) \n\nWeakness\n\n- I could not find a significant weakness of the paper.\n\n\nRating\n\nI like the overall idea of using inter-episode relationships for few-shot training. The proposed approach shows strong performance and technically straightforward and easy to understand. Another strength of the method is that no additional hyper-parameter is used to tune the performance. The paper is clear and extensive experiments support the effectiveness of the paper including supplementary materials.\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "R7iSCVPAks", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "### Summary\nThis paper proposes a way to exploit relationships across tasks in episodic training with the goal of improving the trained models who might be susceptible to poor sampling in for few-shot learning scenarios. The proposed model consists of two components: a cross-attention transformer (CEAM) which is used to observe details across two episodes, and a regularization term (CECR) which imposes that two different instances of the same task (which have the exact same classes) are consistent in terms of prediction. Cross-attention is computed via a scaled-attention transformer using both support and query set. The consistency loss is a knowledge distillation that imposes an agreement on the two episodes. The soft target is chosen among the two predictions selecting the classifier with the highest accuracy.\n### Considerations: \n- I like the idea of exploiting the information across tasks to improve the performance of episodic meta training. This is an interesting direction that should might definitely help disambiguate in the case of poor sampling.\n- The ablation study is accurately performed giving the impression of a careful examination of the components of the model proposed.\n- I'm not sure the authors can claim sota results: here some of the latest models that perform best on mini-imagenet https://paperswithcode.com/sota/few-shot-image-classification-on-mini-1 I would prefer to restate the contribution as an improvement of x% over the baseline. It is obvious that sota performance requires higher capacity models such as dense-net. I think that other experiments are needed in order to make the claim of achieving sota, otherwise, if the claim is changed, I'm satisfied with the experiments.\n- I suggest the authors moving algorithm 1 in the main paper, maybe replacing the verbose description of each step with actual formulas and pseudocode.\n- I think there is still room for improvement on the manuscript.  The paper might be a good contribution to the scientific community, but I'll wait for the authors' response on my doubts before my final decision. \n\n### Questions:\n- Q1: Why only considering tasks with the same classes for consistency? Why not considering also partial overlapping of classes across tasks? I guess it is only for simplicity, but it might be beneficial to consider other types of relationships.\n- Q2: It is not exactly clear how the meta-test evaluation is performed. I understand that during training you always consider a pair of episodes that are used to transform the features, but how does this translate at meta-test time? Do you always need a pair of episodes? My guess is no, but not using the transformer should change the distribution of the features at test-time and I don't find it trivial to see how this is taken into account. Maybe I'm just misreading the paper. I suggest the authors clarifying this point in the paper.\n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper explores the effect of poorly sampled episodes in few-shot learning, and its effect on trained models. The improvements from the additional attention module (CEAM) and regularizer (CECR) are strong, and the ablations are thorough. The reviewers are not fully convinced that poor sampling is indeed the main issue. That is, it could be that CEAM and CECR improve performance for other reasons, but the hypothesis is sensible, and the reviewers believe a more thorough investigation is beyond the scope of this work.

During discussions, one note that came up is whether CEAM works because of cross-episode attention, or if the idea of an instance-level FEAT is itself a good one. One ablation to sort this out would be to apply FEAT and an instance-level FEAT on episodes that are twice as large as those seen by CEAM so that the effective episode size is the same. This would help answer: is it the reduced noise due to effectively larger episodes, a stronger attention mechanism using instance-level information, or is the idea of crossover episodes indeed the important factor? The reviewers agree that this baseline, or an analogous baseline, should be included in the final version.

Decision: Accept (Poster)