Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Authors: Hung-Yu Tseng
Hsin-Ying Lee
Jia-Bin Huang
Ming-Hsuan Yang
Venue:  (2020)

Abstract: Few-shot classification aims to recognize novel categories with only few labeled images in each class. Existing metric-based few-shot classification algorithms predict categories by comparing the feature embeddings of query images with those from a few labeled images (support examples) using a learned metric function. While promising performance has been demonstrated, these methods often fail to generalize to unseen domains due to large discrepancy of the feature distribution across domains. In this work, we address the problem of few-shot classification under domain shifts for metric-based methods. Our core idea is to use feature-wise transformation layers for augmenting the image features using affine transforms to simulate various feature distributions under different domains in the training stage. To capture variations of the feature distributions under different domains, we further apply a learning-to-learn approach to search for the hyper-parameters of the feature-wise transformation layers. We conduct extensive experiments and ablation studies under the domain generalization setting using five few-shot classification datasets: mini-ImageNet, CUB, Cars, Places, and Plantae. Experimental results demonstrate that the proposed feature-wise transformation layer is applicable to various metric-based models, and provides consistent improvements on the few-shot classification performance under domain shift.

Published as a conference paper at ICLR 2020
CROSS-DOMAIN FEW-SHOT CLASSIFICATION VIA LEARNED FEATURE-WISE TRANSFORMATION
Hung- Yu Tseng
Hsin-Ying Lee University of California, Merced hlee24 deucmered. edu
University of California, Merced htseng 6 @ucmerced.... edu
Jia-Bin Huang
Ming-HSsun Yang
Virginia Tech jbhuangevt. edu
University of California, Merced
Google Research
Yonsei University
mhyang@ucmercced.ee.
ABSTRACT
Few-shot classification aims to recognize novel categories with only few labeled images in each class. Existing metric-based few-shot classification algorithms pre- dict categories by comparing the feature embeddings of query images with those from a few labeled images (support examples) using learned metric function. While promising performance has been demonstrated, these methods often fail to generalize to unseen domains due to large discrepancy of the feature distribution across domains. In this work, we address the problem of few-shot classification under domain shifts for metric-based methods. Our core idea is to use feature- wise transformation layers for augmenting the image features using affine transforms to simulate various feature distributions under different domains in the training stage. To capture variations of the feature distributions under different domains, we further apply a learning-to-lean approach to search for the typer-parameten of the feature-wise transformation layers. We conduct extensive experiments and ablation studies under the domain generalization setting using five few-shot clas- sification datasets: mini Imai-em CUB, Cars, Places, and Plantae. Experimental results demonstrate that the proposed feature-wise transformation layer is appli- cable to various metric-based models, and provides consistent improvements on the few-shot classification performance under domain shift.
INTRODUCTION
Few-shot classification (Lake et al., 2015) aims to recognize instances from novel categories (query instances) with only few labeled examples in each class (support examples). Among various recent approaches for addressing the few-shot classification problem, metric-based meta-learning meth- ods (Garcia & Bruna, 2018; Sung et al., 2018; 8; Vinyals al., 2016; Snell al., 2017: Oreshkin al., 2018) have received considerable attention due to their simplicity and effectiveness. In general, metric-based few-shot classification methods make the prediction based on the similarity between the query image and support examples. As illustrated in Figure 1, metric-based approaches con- sist of 1) feature encoder and 2) a metric function. Given an input task consisting of few labeled images (the support set) and unlabeled images (the query set) from novel classes, the encoder first extracts the image features. The metric function then takes the features of both the labeled and unlabeled images input and predicts the category of the query images. Despite the success of recognizing novel classes sampled from the same domain as the training stage (e.g.., both train- ing and testing are mini- mini-Imaged ImageNet classes), Chen et al. (Chen al.. 2019a) recently raise the issue that existing cettric-based approaches often do not generalize well to categories from different domains. The generalization ability to unseen domains, however, is of critical importance due to

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Support (7,)
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(a) Training
Support S
Suppori
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(b) Testing on seen domain
(c) Testing on domains
Figure I: Problem formulation and motivation. Metric-based meta-learning models usually con-
sist of feature encoder and metric function M. We aim to improve the generalization ability of the models training from seen domains to arbitrary unseen domains. The key observation is that the distributions of the image features extracted from tasks in the unseen domains are significantly different from those in the seen domains.
the difficulty to construct large training datasets for rare classes (e.g.. recognizing rare bird species in fine- one-grained classification setting). As result, understanding and addressing the domain shift problem for few-shot classification is of great interest.
To alleviate the domain shift issue, numerous unsupervised domain adaptation techniques have been proposed (Pan & Yang, 2010; Chen et al., 2018; Tzeng al., 2017). These methods focus on adapting the classifier the same category from the source to the target domain. Building upon the domain adaptation formulation, Dong and Xing (Dong & Xing, 2018) relax the constraint and transfer knowledge across domains for recognizing novel category in the one-shot setting. However, unsupervised domain adaptation approaches assume that numerous unlabeled images in the target domain during training. In many cases, this assumption may not be realistic. For example, the cost and efforts of collecting numerous images of rare bird species can be prohibitively high. On the other hand, domain generalization methods have been developed (Blanchard et al., 2011; Li et al., 2019) to learn classifiers that generalize well multiple unseen domains without requiring the access to data from those domains. Yet, existing domain generalization approaches aim at recognizing instance from the same category in the training stage.
available
In this paper, we tackle the domain generalization problem for recognizing novel category in the few-shot classification setting. As shown in Figure 1(c), our key observation is that the distributions of the image features extracted from the tasks in different domains are significantly different. As result, during the training stage, the metric function may overfit to the feature distributions encoded only from the seen domains and thus fail to generalize unseen domains. To address the issue, we propose to integrate feature-wise transformation layer to modulate the feature activations with affine transformations into the feature encoder. The use of these feature- wise transformation layers allows us to simulate various distributions of image features during the training stage, and thus improve the generalization ability of the metric function in the testing phase. Nevertheless, the hyper-parameten of the feature-win -iit transformation layers may require meticulous hand-tuning due to the difficulty model the complex variation of the image feature distributions across various domains. In light of this, we develop a learning-to-lean algorithm to optimize the proposed feature-wise transformation layers. The core idea is optimize the feature-wise transformation layers so that the model can work well on the unseen domains after training the model using the seen domains. We make the source code and datasets public available simulate future research in this field."
We make the following three contributions in this work:
We propose to use teature-wise transformation layers to simulate various image feature dis- tributions extracted from the tasks in different domains. Our feature-wise transformation
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layers methoddaggossin and can be applied to various metric-based few-shot classifica- tion approaches for improving their generalization to unseen domains.
We develop Marming-to-leam method to optimize the hyper-parament of the feature- wise transformation layers. In contrast to the exhaustive parameter hand-tuning process, the proposed pearningtto-lear algorithm is capable of finding the nyper-parameters for the feature-wise transformation layers to capture the variation of image feature distribution across various domains.
We evaluate the performance of three metric-based few-shot classification models (includ- ing MatchingNet (Vinyals et al., 2016), RelationNet (Sung et al., 2018), and Graph Neural Networks (Garcia & Bruna, 2018) 8)) with extensive experiments under the domain general- ization setting. We show that the proposed feature-wise transformation layers can effec- tively improve the generalization ability of metric-based models to unseen domains. We also demonstrate further performance improvement with our learning-to-lest scheme for learning the feature- wise transformation layers.
2 RELATED WORK
Few-shot classification. Few-shot classification aims to learn to recognize novel categories with a limited number of labeled examples in each class. Significant progress has been made using the meta-learning based formulation. There are three main classes of meta-learning approaches for addressing the few-shot classification problem. First, recurrent-based frameworks (Rezende et al., 2016; Santoro et al., 2016) sequentially process and encode the few labeled images of novel categories. Second, optimization-basee schemes (Finn et al., 2017; Rusu et al., 2019; Tseng et al., 2019; Vuorio et al., 2019) Iearn to fine-tune the model with the few example images by integrating the fine-tuning process in the meta-training stage. Third, metric-based methods (Koch et al., 2015; Vinyals et al., 2016; Snell et al., 2017; Oreshkin et al., 2018; Sung et al., 2018: Lifchitz et al., 2019) classify the query images by computing the similarity between the query image and few labeled images of novel categories.
Among these three classes, metric-based methods have attracted considerable attention due their simplicity and effectiveness. Metric-based few-shot classification approaches consist of 1) a feature encoder to extract features from both the labeled and unlabeled images and 2) metric function that takes image features as input and predict the category of unlabeled images. For example, MatchingNet (Vinyals et al., 2016) applies cosine similarity along with a recurrent network, Pro- toNet (Snell et al., 2017) utilizes euclidean distance, RelationNet (Sung et al., 2018) uses CNN modules, GNN (Garcia Bruna, 2018) employs graph convolution modules as the metric func- tions. However, these metric functions may fail to generalize to unseen domains since the distribu- tions of the image features extracted from the task in various domains can be drastically different. Chen et al. (Chen et al., 2019a) recently show that the performance of existing few-shot classification methods degrades significantly under domain shifts. Our work focuses on improving the general- ization ability of metric-based based few-shot classification models to unseen domains. Very recently, Triantafillou et al. (Triantafillou et al., 2020) also target on the cross-domain few-shot classification problem. We encourage the readers to review for a more complete picture.
Domain adaptation. Domain adaptation methods (Pan & Yang, 2010) aim to reduce the domain shift between the source and target domains. Since the emergence of domain adversarial neural net- works (DANN) (Ganin et al., 201 6), numerous frameworks have been proposed to apply adversarial training to align the source and target distributions on the feature-level (Tzeng et al., 2017: Chen et al., 2018: Hsu et al., 2020) or on the pixel-level (Tsai et al., 2018; Hoffman et al., 2018; Bous- malis et al., 2017: Chen al., 00;9;; Lee al., 2018). Most domain frameworks, however, target at adapting knowledge of the same category learned from the source to target domain and thus are less effective to handle novel category as in the few-shot classification scenarios. One exception is the work by Dong and Xing (Dong& & Xing, 2018) that address the domain shift issue in the one-shot learning setting. Nevertheless, these domain adaptation methods require access to the unlabeled images in the target domain during the training. Such an assumption may not be feasible in many applications due to the difficulty of collecting abundant examples of rare categories (c.g., rare bird species).
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Domain generalization. In contrast to the domain adaptation frameworks, domain generaliza- tion (Blanchard et al., 2011) methods aim at generalizing from a set of seen domains to the un- seen domain without accessing instances from the unseen domain during the training stage. Before the emerging of learning-to-leat approaches, several methods have been proposed for tackling the domain generalization problem. Examples include extracting domain-invariane features from various seen domains (Blanchard et al., 2011; Li al., 2018b; Muandet et al., 2013), improving the classifiers by fusing classifiers learned from seen domains (Niu et al., 2015a;b), and decomposing the classifiers into domain-spedne components (Khosla et al., 2012; Li al., 2017a). Another stream of work learns to augment the input data with adversarial learning (Shankar et al., 2018; Volpi et al., 2018). Most recently, a number of methods apply the learning-to-leat strategy to simulate the generalization process in the training stage (Balaji et al., 2018; Li et al., 2018a; 2019). Our method adopts a similar approach to train the proposed feature-wist wise transformation layers. The application context, however, differs from prior work as we focus on recognizing novel category from unseen domains in few-shot classification. The goal of this work is to make few-shot classification algorithms robust to domain shifts.
(i.e., meta-learning) (Ravi & Larochelle, 2017; Finn et al., 2017)
and
Learning-based data augmentation. Data augmentation methods are designed to increase the diversity of data for the training process. Unlike the hand-crafted approaches such as horizontal flip- ping and random cropping, several recent approaches have been proposed to learn the data augmen- tation (Cubuk et al., 2019; DeVries & Taylor, 2017a; Lemley et al., 2017; Perez & Wang, 2017: Sixt et al., 2018; Tran et al., 2017). For instance, the SmartAugmentation (Lemley et al., 2017) scheme trains a network that combines multiple images from the same category. The Bayesian DA (Tran et al., 2017) method augments the data according the distribution learned from the training set, and the RenderGAN (Sixt et al., 2018) model simulates realistic images using generative adversarial networks. In addition, the AutoAugment (Cubuk et al., 2019) algorithm learns the augmentation via reinforcement learning. Two recent frameworks (Shankar et al., 2018; Volpi et al., 2018) target at augmenting the data by modeling the variation across different domains with adversarial learn- ing. Similar to these approaches for capturing the variations across multiple domains, we develop a learning-to to-rearn process optimize the proposed feature- reature-wise transformation layers for simulating various distributions of image features encoded from different domains.
Conditional normalization. Conditional normalization aims to modulate the activation via a leamed affine transformation conditioned external data (e.g., an image of artwork for cap- turing a specific style). Conditional normalization methods, including Conditional Batch Normal- ization (Dumoulin al., 2017), Adaptive Instance Normalization (Huang & Belongie, 2017), and SPADE (Park et al., 2019), are widely used in the style transfer and image synthesis tasks (Karras al., 2019; Lee et al., 2020; AlBahar & Huang. 2019). In addition image stylization and genera- tion. conditional normalization has also been applied to align different data distributions for domain adaptation (Cariucci et al., 2017: Li et al., 2017b). In particular, the TADAM method (Oreshkin et al., 2018 8) applies conditional batch normalization to metric-based models for the few-shot clas- sification task. The TADAM method aims to model the training task distribution under the same domain. In contrast, we focus on simulating various features distributions from different domains.
Regularization for neural networks. Adding some form of randomness in the training stage is an effective way to improve generalization (Srivastava al., 2014; Wan al., 2013; Larsson al., 2017; DeVries & Taylor, 2017b; Zhang et al., 2018; Ghiasi et al., 2018). The proposed feature- wise transformation layer for modulating the feature activations of intermediate layers (by applying random affine transformations) can also be viewed as way to regularize network training.
3 METHODOL.OGY
3.1 PRELIMINARIES
Few-shot classification and metric-based method. The few-shot classification problem is typ- ically characterized as N., way (number of categories) and N, shot (number of labeled examples for each category). Figure shows an example of how the metric-based frameworks operate in the 3-way 3-shot few shot classification task. A metric-based algorithm generally contains feature
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encoder E and metric function M. For each iteration during the training stage, the algorithm ran- domly samples Nw categories and constructs a task T. We denote the collection of imput images as X' (2, .x, and the corresponding categorical labels as and y {y), "...... : yn ). A task consists of a support set S = [(x, }.,}} and query set Q {(xx Vg) The support set S and query set Q are respectively formed by randomly selecting N₂ and Ng samples for each of the No categories.
The feature encoder E first extracts the features from both the support and query images. The metric function M then predicts the categories of the query images X₂ according to the label of support images Ve. the encoded query image E(x"), and the encoded support images E(X,). The process can be formulated as
B = M(',, F(X)), F(2)))
Finally, the training objective of a metric-based framework is the classification loss of the images in the query set,
== Lels (1:.
The main difference between various metric-based algorithms lies in the design choice for the metric function M. For instance, the Matching (Vinyals et al., 2016) method utilizes long-short-term memories (LSTM), the RelationNet (Sung al., 2018) model applies convolutional neural net- works (CNN), and the GNN (Garcia & Bruna, 2018) scheme uses graph convolutional networks.
Problem setting. In this work, we address the few-shot classification problem under the domain generalization setting. We denote domain consisting of a collection of few-shot classification tasks as T = {Ti, T2... ... ]. J. We assume N seen domains {7,00em T ... .N } available in the training phase. The goal is to learn metric-based few-show classification model using the seen domains, such that the model can generalize well to an unseen domain Jusseen, For example, one can train the model with the mini-ImageNet (Ravi & Larochelle, 2017) dataset well as some public available fine-grained few-shot classification domains, e.g., CUB (Welinder et al., 2010), and then evaluate the generalization ability of the model problem formulation does not access images in the unseen domain at the training stage.
unseen plants domain. Note that our
3.2 FEATURE- WEASE TRANSFORMATION LAYER
Our focus in this work improve the generalization ability of metric-based few-shot classification models to arbitrary unseen domains. As shown in Figure 1, due to the discrepancy between the feature distributions extracted from the task the seen and unseen domains, the metric function M may overfit to the seen domains and fail to generalize to the unseen domains. To address the problem, we propose to integrate feature-wise transformation to augment the intermediate feature activations with affine transformations into the feature encoder E. Intuitively, the feature encoder E integrated with the feature- wise transformation layers can produce more diverse feature distributions which improve the generalization ability of the metric function M. As shown in Figure 2(b), insert the feature-wise transformation layer after the batch normalization layer in the feature encoder E. The hyper-parameters 0g E RCx1x1 and 03 Rxx1x1 indicate the standard deviations of the Gaussian distributions for sampling the affine transformation parameters. Given an intermediate feature activation map z in the feature encoder with the dimension of C x H x W, we first sample the scaling term y and bias term / from Gaussian distributions.
7 softplus(0,))
B ~ N(, softplus(0:))..
(3)
We then compute the modulated activation 2 as
2,, = %o X 2e,h,w + Bor
(4)
where we,b,w E and Zc.h.u In practice, we insert the feature-wise transformation layers the feature encoder E at multiple levels.
3.3 LEARNING THE FEATURE- WISE TRANSFORMATION LAYERS
While we can empirically determine spper-parameters 0, = {0,, 0,} of the feature-wise transfor- mation layer, it remains challenging to hand-tune generic of parameters which are effective
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Pseudo-seen (ps)
Feature-wise transformation layer
(0 1A
mini-lmageNet
2 E RCxHXW
et
M oh
L - 05*1 1
Y E RCXIXI
(Eqn. 5)
REXINI
Pseudo-unseen (pu) CUB
y-N(1, softplus(8,)
E oggt
M Mott
Bf (()c++ 14
B B-N((O, sofppuuu)x)(()))
(Eqn. 7)
00 0g)
(a)
(b)
Figure 2: Method overview. (a) We propose a feature-wise transformation layer modulate in- termediate feature activation in the feature encoder E' with the scaling and bias terms sampled from the Gaussian distributions parameterized by the hyper-parametee 0,, and 0g. During the train- ing phase, we insert collection of feature-wiss transformation layers into the feature encoder to simulate feature distributions extracted from the tasks in various domains. (b) We design learning- to-learn algorithm to optimize the hyper-parameter 0,, and 03 of feature-wise transformation layers by maximizing the performance of the applied metric-based model on the pseudo-unseen domain (bottom) after it is optimized on the pseudo-seen domain (top).
on different settings (i.e., different metric- based frameworks and different seen domains). To ad- dress this problem, we design a learning- to-l--ttt-eamm algorithm to optimize the hyper- parameters of the feature-wise transformation layer. The core idea that training the metric-based model integrated with the proposed layers on the seen domains should improve the performance of the model on the unseen domains.
We illustrate the process in Figure 2(b) and Algorithm 1. In each training iteration f, we sam- ple pseudo-seen domain TPs and a pseudo-unseen domain T from a set of seen domains (Treem T :... TT Given metric-based based model with feature encoder Egg and metric fune- tion Mo first integrate the proposed layers with arpempparaeeters 0f {0%, f3} into the feature encoder (i.e., Egg,o). We then use the loss in equation 2 to update the parameters in the metric-based model with the pseudo-seen task T ((XP5 )/*)), (XPS )/ E TPs, namely
(0.+1, 100100++1 (0', (%,, Vo Lote Mo (J Eog (XT*), Eog.o (xT* (5)
where a is the learning rate. We then measure the generalization ability of the updated metric-based model by 1) removing the feature-wiss transformation layers from the model and 2) computing the classification loss the updated model on the pseudo-unseen task 7 {(XP" "/"",, (Xg0) ))")) TP", namely
D Leta Most (D/D (xp") Eggti ( (xT"
Finally, as the loss P reflects the effectiveness of the feature-wise transformation layers, we opti- mize the hyper-parameters 0 by
0
0
Voz LP"
Note that the metric- based model and feature- wise transformation layers are jointly optimized the training stage.

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Algorithm 1: Learning-to-Leam Feature-Wiss Transformation.
Require:
Seen domains {Tpeen TT2ee, _ ), learning rate (
Randomly initialize o, 0m and 0f
while training do
Randomly sample non-overlapping pseudo-seen 7 and psuedo-unseen 7 domains from the seen domains
Sample pesudo-seen task 7 E T and a pseudo-unsa unseen task 7Pu T
// Update metric-based model with pseudo-seen task: Obtain 0++1, 0 using equation 5
// Update feature-wise transformation layers with pseudo-unseen task: Obtain 0++1 using equation and equation 7
10 end
4
EXPERIMENTAL RESULTS
4.1 EXPERIMENTAL SETUPS
We validate the efficacy of the proposed feature-wise transformation layer with three existing metric- based algorithms (Vinyals et al., 2016; Sung et al., 2018; Garcia & Bruna, 2018) under two experi- mental settings. First, we empirically determine the hyper- hyper-parameters 0 = {8., 83} of the feature- wise transformation layers and analyze the impact of the feature-wise transformation layers. We train the few-shot classification model on the mini-ImageNet (Bousmalis et al., 2017) domain and evaluate the trained model four different domains: CUB (Welinder et al., 2010), Cars (Krause et al., 2013), Places (Zhou et al., 2017), and Plantae (Van Horn et al., 2018). Second, we demon- strate the importance of the proposed learning-to-ler of feature-wise transformation layers. We adopt the leave-one-out setting by selecting an unseen domain from CUB, Cars, Places, and Plantae domains. The mini-ImageNNe (Bousmalis al., 2017) and the remaining domains then serve as the seen domains for training both the metric-based model and feature- wise transformation layers using Algorithm After the training, we evaluate the trained model on the selected unseen domain.
scheme for optimizing the hyper-parameters
Datasets. We conduct experiments using five datasets: mini-ImageNet (Ravi & Larochelle, 2017), CUB (Welinder et al., 2010), Cars (Krause et al., 2013), Places (Zhou et al., 2017), and Plan- tae (Van Horn et al., 2018). Since the mini-ImageNNet dataset serves as the seen domain for all experiments, select the training iterations with the best accuracy on the validation set of the mini- ImageNet dataset for evaluation. More details of dataset processing are presented in Appendix A.
Implementation details. We apply the feature- wise transformation layers to three metric-based frameworks: MatchingNet (Vinyals et al., 2016), RelationNet (Sung et al., 201 8), and GNN (Garcia & Bruna. 2018). We use the public implementation from Chen al. (Chen et al., 2019a) train both the MatchingNet and RelationNet model.² For the GNN approach, we integrate the official implementation for graph convolutional network into Chen's implementation." In all experiments, we adopt the ResNet-10 10 (He et al., 2016) model as the backbone network for our feature encoder E.
We present the average results over 1, 000 trials for all the experiments. each trial, we randomly sample N., categories (e.g., 5 classes for 5-way classification). For each category, we randomly select N₂ images (e.g.. 1-shot or 5-shot) for the support set X, and 16 images for the query set Xg- We discuss the implementation details in Appendix A.2.
Pre-trained feature encoder. Prior to the few-shot classification training stage, we first pre- pre-train the feature encoder E' by minimizing the standard cross-entropy classification loss the 64 training categories in the mini-lmageNet dataset. This strategy can significantly improve the performance of
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Table 1: Few-shot classification results trained with the mini- Lmai--m dataset. We train the model on the mini- Muni-ImageNet domain and evaluate the trained model on another domain. FT in- dicates that we apply the feature-wise transformation layers with empirically determined hyper- parameters to train the model.
S-way S-hh
FT
mini ImageNet
CUB
Cars
Places
Plantae
MatchingNet
39.10 + 0.61%
35.89 0.51%
30.77 + 47%
0.79%
32.70 + 0.60%
58.76 +: 0.61%
38.61 + 0.53%
29.82 + 0.44%
51.07 1 0.68%
34.48 : 0.50%
RelationNet
57.80 + 0.88%
12.44 = 0.77%
29.11 + 0.60%
18.61 0.83%
33.17 + 0.64%
58.64 + 0.85%
44.07 + 0.77%
28.63 + 0.50%
50.68 + 0.87%
33.14 + 0.62%
GNN
60.77 1: 0.75% 66.32 0.80%
45.69 1. 0.68%
31.79 1. 0.51%
53. : 0.80%
35.60 1 0.56%
0.75%
31.61 + 0.53%
55.77 +: 0.79%
35.95 + 5.58%
5-way 5-Shot
mini-ImageNNt
MatchingNet
70.96 1 0.65%
51.37 1: 0.77%
38.99 1. 0.64%
63.16 1 0.77%
46.53 1 0.68%
72.53 0 0.69%
55.23 + 0.83%
41 + 0.68%
64.55 + 0. 75%
41.69 + 0.63%
RelationNet
71.00 0.69%
57.77 0.69%
37.33 0.68%
63.32 0.76%
44.00 1: 0.60%
73.78 0.64%
59.46 + 0.71
39.91 + 0.69%
66. 28 + 0. 72%
45.08 + 0.59%
GNN
80.87 1 0.56%
62. 25 1 0.65%
44.28 1. 0.63%
70.84 : 0.65%
52.53 1. 0.59%
81.98 + 0.55%
66.98 + 0.68%
44.90 + 0.64%
73.94 1 0.67%
53.85 + 0.62%
metric-based models and is widely adopted in several recent frameworks (Rusu et al., 2019; Gidaris & Komodakis, 2018; Lifchitz al., 2019).
4.2 FEATURE- WISE TRANSFORMATION WITH MANUAL PARAMETER TUNING
We train the model using the mini- mmag--m dataset and evaluate the trained model with four other unseen domains: CUB, Cars, Places, and Plantae. We add the proposed feature- -iite transformation layers after the last batch normalization layer of all the residual blocks in the feature encoder E during the training stage. We empirically set 0, and 0g in all feature-wise transformation layers to be 0.3 and 0.5, respectively. Table I shows the metric-based model trained with the feature- wise transformation layers performs favorably against the individual baselines. We attribute the improvement of generalization to the use of the proposed layers for making the feature encoder E also observe
produce more diverse feature distributions in the training stage. As a by-product, the improvement on the seen domain (i.e., mini-ImageNet) since there still a slight discrepancy between the feature distributions extracted from the training and testing sets of the same domain. is noteworthy that we also compare the proposed method with several recent approaches (c.g.. Lee al. (2019) in Table 8 and Table 9. With the proposed feature- Prature-wise transformation layers, the GNN (Garcia & Bruna, 2018) model performs favorably against the state-of-the-an art frameworks on both the seen domain (i.e., mmagelemaeNee) and unseen domains.
4.3 GENERALIZATION FROM MULTIPLE DOMAINS
Here we validate the effectiveness of the proposed learning-to-lean algorithm for optimizing the hyper-parameters of the feature-wise transformation layers. We compare the metric-model trained with the proposed learning procedure to the model trained with the pre-determined feature-wise transformation layers. The leave-one-out setting is used to select one domain from the CUB, Cars, Places, and Plantae as the unseen domain for the evaluation. The mini-ImageNet and the remaining domains serve as the seen domains for training the model. Since we select the training iteration according to the validation performance on the Imag-Ne domain for evaluation, we do not consider the mini-ImageeNt as the unseen domain. We present the results in Table 2. We denote FT and LFT as applying pre-determined feature-wist wise transformation layers and those layers optimized with the proposed learning-to-led algorithm, respectively. The models optimized with proposed learning scheme outperforms those trained with the pre-determined feature- transformation lay- ers since the optimized reature-wise transformation layers can better capture the variation of feature distributions across different domains. Table 1 and Table 2 show that the proposed feature- wise transformation layers together with the learning-to- leam algorithm effectively mitigate the domain shift problem for metric-based frameworks.
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Table Few-shot classification results trained with multiple datasets. We use the leave-one-our setting to select the unseen domain and train the model as well as the feature-wise transformation layers using Algorithm 1. FT and LFT indicate applying the pre-determined and carning-to-learee
feature-wist wise transformation, respectively.
5-way 1-Shot
CUB
Cars
Places
Plantae
MatchingNet
37.90 0.55%
28.96 0.45%
49.01 0.65%
33.21 0.51%
FT
41.74 0.59%
28.30 = 0.44%
48.77 0.65%
32.15 0.50%
LFT
0.59% 43.29
30.62 1 0.48%
52.51 + 0.67%
35.12 0.54%
RelationNet
44.33 0.59%
29.53 = 0.45%
47.76 + 0.63%
33.76 + 0.52%
FT
44.67 1 0.58%
30.38 = 0.47%
48.40 + 0.64%
35.40 1 0.53%
LFT
48.38 0.63%
32.21 1 0.51%
50.74 1 0.66%
35.00 1 0.52%
GNN
19.16 0.73%
32.95
0.56%
51.39 + 0.80%
37.15 + 0.60%
FT
48.21 + 0.75%
33.26
0.56%
54.81 + 0.81%
37.51 + 0.62%
LFT
51.51
0.80%
34.12 + 0.63%
56.31 0.80%
42. 09 0.68%
5-way 5-Shot
CUB
Cars
Places
Plantac
MatchingNet
51.92 0.80%
39.87 = 0.51%
61.82 1 0.57%
47.29 1 0.51%
FT
56.29 + 0.80%
39.58 = 0.54%
62.32 + 0.58%
46.48 +: 0.52%
LFT
0.57% 61.41
43.08 + 0.55%
64.99 = 0.59%
48.32 + 0.57%
RelationNet
62.13 + 0.74%
10.64 0.54%
64.34 + 0.57%
46.29 + 0.56%
FT
63.64 + 0.77%
42.24 0.57%
65.42 0.58%
47.81 = 0.51%
LFT
64.99 0.54%
43.44 0.59%
67.35 = 0.54%
50.39 0.52%
GNN
69.26 0.68%
48.91
0.67%
72.59 0.67%
58.36 0.68%
70.37 0.68%
47.68
0.63%
74.48 0.70%
57.8
0.68%
LFT
73.11 0.68%
49.88 1 0.67%
77.05 + 0.65%
58. 58.84 0.66%
Seen domains
mini-ImageNer
Cars
Places
Plantae
Unseen domain
CUB
(a) RelationNet
(b) RelationNet FT
(c) RelationNet LFT
Figure 3: T-SNE visualization of the image features extracted from tasks in different domains. We show the t-SNE visualization of the features extracted by the (a) original feature encoder E, (b) feature encoder with pre-determined feature-wise transformation layers, and (c) feature encoder with leaminggttollearee to-lead feature- caature-wiie transformation.
Note since the proposed learning-to Baonnggtt--aann te-learn approach optimizes the hyper-parameterd via stochas- tic gradient descent, may not find the global minimum that achieves the best performance. certainly possible to manually find another set of hyper-parameter setting that achieves better perfor- mance. However, this requires meticulous and computationally expensive 0., and 0g is e, x 1 1 for the i-th feature- wise
ppper-paraetter tuning.
Specifically, the dimension of the transformation layer in the feature encoder, where c, is the number of feature channels. As there feature-wiss transformation layers in the feature encoder E, need to perform the hyper- parameter search (c1 + C2 c,) x 2-dimensional space. In practice, the dimension of the the search space is 1920.
Visualizing Teature-wise transformed features. To demonstrate that the proposed feature- transformation layers can simulate various feature distributions extracted from the task in different domains, show the t-SNE visualizations of the image features extracted by the feature encoder in the RelationNet (Sung et al., 2018) model in Figure 3. The model is trained with 5-way 5-shot classification setting the mini-ImageNet, Cars, Places, and Plantae domains (i.e., corresponding

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Published as a conference paper at ICLR 2020
1.2
112
softplus(
0.6
0.4
04
-34
-44
residual block
residual block
MatchingNet
RelationNet
GNN
Figure 4: Visualization of the feature-wise transformation layers. We show the quartile visual-
ization of the activations softplus(0,) and softplus(0), from each feature-wist transformation layer that are optimized by the proposed learning-to-lese algorithm.
to the fifth block of the second column in Table 2). We observe that the distance between features tracted from different domains becomes smaller with the help of feature-wise transformation layers. scheme can further help the feature-wise transforma-
Furthermore, the proposed learning-to-lest tion layers capture the variation of feature distributions from various domains, thus close the domain gap and improve the generalization ability of metric-based models.
Visualizing feature-wise transformation layers. To better understand how the learned feature- wise transformation layers operate, we show the values of the softplus(0,) and softplus(0,) in the feature-ve Seature-wise transformation layer. Figure 4 presents the visualization. The values of scaling terms softplus(0.,) tend to become smaller
deeper layers, particularly for those the last residual block. On the other hand, the depth of the layer does not seem to have an apparent impact on the distributions of the bias terms softplus(fs). The distributions are also different across different metric-basee classification methods. These results suggest the importance of the proposed learning- to-learn algorithm because there does not exist set of optimal hyper-parameters parameters of the fcature-wist wise transformation layers which work well with all metric-based approaches.
5 CONCLUSIONS
We propose a method to effectively enhance metric-based few-shot classification frameworks under domain shifts. The core idea of our method lies in using the feature- transformation layer to simulate various feature distributions extracted from the tasks in different domains. We develop a learning-to-lean approach for optimizing the hyper-parameters of the feature-wise transforma- tion layers by simulating the generalization process using multiple seen domains. From extensive experiments, we demonstrate that our technique applicable to different metric-based few-shot classification algorithms and show consistent improvement over the baselines.
ACKNOWLEDCEMENTS
This work is supported in part by the NSF CAREER Grant #1 149783, the NSF Grant #1 #1555785, and gifts from Google.
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A APPENDIX
A.1 DATASET COLLECTION
We use five few-shot classification datasets in all of our experiments: mini-ImageNet, CUB, Cars, Places, and Plantae. We follow the setting in Ravi & Larochelle (2017) and Hilliard al. (2018) to process mini- ImageNet and CUB datasets. As for the other datasets, we manually process the dataset by random splitting the classes. The number of training. validation, testing categories for each dataset are summarized in Table 3.
Table 3: Summarization of the datasets (domains). We additionally collect and split the Cars, Places, and Plantae datasets.
Datasets
mini ImageNet
CUB
Cars
Plaecs
Plantee
Source
Dong et al. (2009)
Welinder et (2010)
Kranse et all (2013)
al. (2017)
Van Horn et all (2018)
Traininn categories
64
100
58
183
100 50
Validation entegories
16
50
49
91
Tosting estegories
50
Split setting
Ravi Lanochelle (2017)
Hilliard et (2018)
randomly split
randomly split
randomly split
A.2 ADDITIONAL IMPLEMENTATION DETAILS
We use the implementation and adopt the setting of hyper-panee paree--erseterss from Chen et al. (Chen et al., 2019a).* We train the metric-based model and reature-wise transformation layers with a learning rate of 0.001 and 40, 000 iterations. For feature- casture-wiie transformation layers, we apply L2 regularization with a weight of 10-8, The number of inner iterations adopted in the learning-to-lean scheme is set to be 1.
Matching Network. We cannot utilize the MatchingNet implementation from Chen et al. (Chen et al., 2019a) since they applied the Pytorch built-in LSTT module, which does not support second- order backpropagation. Without the second-order backpropagation, we are unable to optimize the feature augmentation layers using the proposed learning-to-learn algorithm. As result, we re- implement the LSTM module for the MatchingNet model. To verify the correctness of our imple- mentation, we evaluate the 5-way 5-shot performance with the ResNet- 10 (He et al., 2016) back- bone network on the mini-ImageNNe dataset (Ravi & Larochelle, 2017). Our implementation ports 68.88 + 0.69% accuracy, which is similar to the result (i.e., 68.82 + 0.65%) reported by Chen al. (Chen et al., 2019a). We make the source code and datasets public available to foster future progress in this field.5
A.3 ADDITIONAL EXPERIMENTAL RESULTS
Ablation study on pre-trained metric encoder. As described in Section 1, we pre-trained the metric encoder by minimizing the cross-entropy classification loss using the 64 training cate- gories from the mini- ImageNet dataset. To understand the impact of the pre-training, we conduct an ablation study using the leave-one-out experiment illustrated Section 4.3. As shown in Table 4, pre-training the metric encoder E substantially improve the few-shot classification performance of metric-based frameworks. Note that such a pre-training process also adopted by several recent frameworks (Rusu et al., 2019; Gidaris & Komodakis, 2018; Lifchitz et al., 2019) to boost the few- shot classification performance.
Number of ways in testing stage. In this experiment, we consider a practical scenario that the number of ways N., in the testing phase is different from that in the training stage. Since the GNN (Garcia & Bruna, 2018) framework requires the numbers ways to be consistent in the train- ing and testing, we evaluate the MatchingNet (Vinyals et al., 2016) and RelationNet (Sung et al., 2018) model with this setting. Table reports the performances of the models trained on the mini- ImageNet, Cars, Places, and Plantae domains under the 5-way 5-shot setting (i.c., corresponding
*https: pttrps://giiie 'https: :/gi thub. hht.b.ccw/yyrteenggoo///rrooommm wntimaaiiieiieeie nrewShot
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Table 4: Ablation study on pre-trained metric encoder. We conduct leave-one-out setting to select the unseen domain to study the effectiveness of pre-training the feature encoder E on the mini-lmageNet dataset.
1-Shot
Pre-trained
CUB
Cars
Places
Plantae
NatchingNet
37.37 + 0.53%
30.60 1 0.51%
41.42 1 0.59%
31.93 1 0.51%
37.90 1 0.55%
28.96 1 0.45%
49.01 1 0.65%
33.21 1 0.51 %
RelationNet
38.46 1 0.56%
30.77 1 0.51%
37.49 1 0.58%
32.86 1 0.53%
/
44.33 1 0.59%
29.53 + 0. 0.45%
47.76 1 0.63%
33.76 1 0.52%
GNN
37 + 0.63%
29.01 + 0.56%
36.06 + 0.62%
34.99 + 0.63%
49.46 + 0.73%
32.95 + 0.56%
51.39 + 0.80%
37.15 + 0.60%
5-Shot
Pre-trained
CUB
Cars
Places
Plantae
MatchingNet
49.83 + 0.55%
39.41 0.53%
59.18 0.60%
43.53 1 0.53%
51.92 1 0.80%
39.87 + 0.51%
61.82 0.57%
47.29 1 0.51% %
RelationNet
55.85 + 0.55%
42.55 + 0.58%
59.85 + 0.54%
45.24 + 0.55%
62.13 + 0.74%
10.64 + 0.54%
64.34 + 0.57%
16.29 + 0.56%
GNN
60.13 = 0.64%
43.60 + 0.67%
56.67 + 0.64%
49.17 + 0.62%
69.26 + 0 0.68%
48.91 + 0.67%
72.59 + 0.67%
58.30 28.36-2 = 0 0.68%
Table 5: Few-shot classification results under various numbers of ways in testing stage. We compare the 5-shot performance under various number of ways in the testing phase. The CUB dataset is select as the testing (unseen) domain. All the models are trained with 5-way 5-shot setting.
Shot
CUB 2-way
CUB 5-way
CUB 10-way
CUB 20-way
MatchingNet
78.46 + 0. 7.7%
51.92 + 0.80%
38.22 + 0.38%
26.17 + 0.24%
FT
80.74 + 0.77%
56.29 + 0.80%
41.09 0.39%
29.19 + 0.24%
LFT
83.88
0.72%
61.41 + 0.57%
0.39% 45.69
32.81 + 0.23%
RelationNet
84.25 1 0.72%
62.13 1 0.74%
47.15 0.40%
34.52 + 0.24%
85.48 0.69%
63.64 1 0.77%
48.35 0.38%
35.30 1 0.24%
LFT
85.44 = 0.72%
64.99 = 0.54%
49 19.90 + 0.40%
37.20 = 0.25%
to the fourth and fifth block of the second column in Table 2). Our proposed learning-to-laane feature-wise transformation layers are capable of improving the generalization of metric-based mod- els to the unseen domain under various numbers of ways in the testing stage.
Pre-determined papar-parameters of feature-wise transformation layers. We demonstrate the difficulty to hand-tune the this experiment. Different from the setting described in Section 4.2, set the -ypar-parameters 0, and 03 in all feature-wise transformation layers to be 1. The model is trained under 5-way setting using the mini-lma domain, and evaluate it the other domains. We report the 1- shot and 5-shot performance in Table 6. We denote applying feature-wist transformation layers with {0,,
of the proposed feature-wise transformation layers in
{0.3, 0.5) as FT and those with {0,,0g} [1,1} as FT*. We observe that the metric-based models applied with FT perform favorably against to those applied with FT* In several cases, applying FT even yields inferior results compared to the original training without the feature-wist wise transformation layers. This suggests the difficulty of hand-tuning the typer-parameters and the importance of the proposed learning-to-lest scheme for optimizing the hyper-parametens of the feature-wise transformation layers.
Hyper-paramedl initialization for learning-to-learn. For all the experiments, we initialize the parameters 0, and 0g to 0.3 and 0.5, which we empirically determine in Section 4.2, to train the feature-wist wise transformation layers. In practice, we find that the cross-domain performance is not sensitive as long the initialized values are within the same order (e.g.. 0.1 and 0.3). Here report the results of training the RelationNet with the initialization (0.,. 0₂} = (.3.. In this experiment, the CUB dataset as the unseen domain for evaluation and conduct the training
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Table 6: Few-shot classification results by applying different pre-determined hy hyper-parameters of feature-wise transformation layers. We train the model on the mini- Imageeeteeeet with a different set of pre-determined hyper-parametert of feature- transformation layers. FT and FT indicate that we apply the feature-wit transformation layers with syper--arrameter' (8.. 03) to be {0.3, 0.5}
and {1, 1), respectively.
1-Sbot
mini-ImageNee
CUB
Cars
Places
Plantae
MatchingNet
FT
58.76 + 0.61%
36.61 + 0.53%
29.82 0.441%
51.07 + 0.08%
33 -8 + 0.50%
FT
51.66 + 0.641%
31.74 + 0.51%
27. U. 0.41%
45.01 + 0.64%
28.73 + 0.42%
RelationNet
FT
58.64 + 0.85%
44.07 + 0.77%
28.63 = 0 0.39%
50.68 + 0.87%
33. 1-.14 + 0, 0.62%
FT
57.45 + 0.66%
+ 0.53%
29.15 0.45%
0.64%
33.21 0.47%
GNN
FT
06.32 + 0.80%
47.47 + 0.75%
31. 61.61-1 0 0.53%
55.77 + 0.79%
35.95 + 0.58%
FT
62.63 + 0.76%
44.61 + 0.66%
31.56
0.52%
53.39 + 0.74%
36.73 + 0.57%
5-Sbot
mini-ImageNet
CUB
Cars
Places
Plantac
MatchingNet
FT
72.53 + 0.69%
55 + 0.83%
41.24 0.63%
64.55 + 0.75%
41.69 + 0. 0.63%
FT
+ 0.60% 61.93
42.83 + 0.61%
32.19 0.48%
59.47 + 0.63%
39.61 + 0.19%
RelationNet
FT
73.78 + 0.64%
69 69.46 + 0.71%
39.91 0.69%
66.25 + 0. 72%
45 + 0.59%
FT
72.79 + 0.6.1%
59.18 + 0.57%
10.54 0 0.3/1%
13 + 0. 0.52%
13.61 + 0.49%
GNN
FT
81.98
+ 0.55%
66 98 + 0.68%
44.90 = 0. 0.645%
73.94 + 0.67%
53 85 + 0.62%
62.40 1: 0.58%
66.33 1 0.73%
47.63 0.64%
75.48 d: 0.65%
51.92 d: 0.59%
Table 7: Few-shot classification results by applying the learning-to-learu approach trained with single seen domain. We attempt conduct the proposed learning-to- to-lerr-t- trainin with as singe seen domain, denoted as LFT* We train the model using the mini-ImageNee dataset and report the 5-way 5-ohot classification accuracy.
5-way S-Shot
mini- NetgeNet
CUB
Cars
Places
Plantne
RelationNet
FT
73.78 1 0.64%
59.46 1: 0.71%
39.91 1. 0.69%
66.28 + 0.72%
45.08 1 0.59%
RelationNet
L.T
73.50 + 0.50%
58.19 0.52%
39.35 0.54%
66. = 0.57%
46.75 +: 0.51%
described in Algorithm 1. The 5-way 5-shot classification accuracy on the CUB dataset is 64.79 + 0.55%, which is similar to the one we report in Table 2 (i.e., 64.99 = 0.54%).
Learning-to-lear using a single domain. The proposed Barning-to-leane method requires mul- tiple domains for training. Here we apply the learning-to-kee to-learn method based on one single domain. More specifically, we randomly sample two different tasks from the mini- Imageet dataset in each iteration of the training process described in Algorithm 1. One task serves as the pseudo seen task, while the other one serves as the pseudo-unseen task. We train the RelationNet model with the above-mentioned setting on 5-way 5-shot classification using the mini- Aini-ImageNet dataset only. As shown in Table 7, the performance improvement of the RelationNet model is not significant com- pared to the model trained with pre-determined hyper-parameters {0.,. 03} {0.3, 0.5). This sug- gests that utilizing one single domain for learning the feature-wise transformation is not as effective as that using multiple domains (demonstrated in Table 2). The reason is that during the training phase, the discrepancy of the feature distributions extracted from the psudo-seen and pseudo-unseen tasks is not as significant since these two tasks are sampled from the same domain. As result, the hyper-parameter, (0,, 0g} cannot be effectively optimized to capture the variation of feature distributions sampled from various domains.
Comparison with the state-of-the-ar few-shot classification on the mini-ImageNNet We com- pare the metric-based frameworks applied with the proposed feature- wise transformation layers to the state-of-ther oftt--of-hheartt few-shot classification methods in Table 8. In this experiment, we train the model with the pre-determined papar-parameters of feature-wise transformation layers on the training set of the mini-ImageNet (Ravi& & Larochelle, 2017) dataset. Note that we do not use the learned version of the feature-win transformation layers in the training to ensure fair comparison. Combining Ta- ble 4 and Table 8. observe that the metric-based frameworks train with 1) pre-trained feature encoder, and 2) feature-wise transformation layers with carefully hand -unded hyper-paramen parameters can demonstrate competitive performance.
17

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Published as a conference paper at ICLR 2020
Table Comparison to the state-of-the-art few-shot classification algorithms. We compare the metric-based frameworks applied with the proposed feature-wist wise transformation layers using pre- determined
{0y, {s} (0.3, 0.5) (denoted FT) to other state-of-the- few-shot classification methods. Note that all the methods are trained only on the mini-ImageNe dataset. To ensure fair comparisons with other methods, we are unable to use the learned version of the feature- wise transformation layers described in Section 3.3. By augmenting existing metric-based few-shot classification models with the proposed feature-wise transformation layer, we obtain competitive performance when compared with many recent and more complicated methods. The best results in each block are highlighted bold.
backbone
method
5-way 1-shot
5-way 5-shot
ResNet-11
TADAM (Oreshkin et al., 2018)
58.50 + 0.30%
76.70 +: 0.30%
DC (Lifchitz et al., 2019)
62.53 0.19%
78.95 + 0.13%
DC + IMP (Lifchitz al., 2019)
79.77 0.19%
MetaOptNet--S M-ee---V--rrrivvll (Lee et al., 2019)
64.09 + 0 0.62%
80.00 0.45%
WRN-28
Qiao et al. (2018)
59.60 = 0.41%
77.74 + 0.19%
I.FO (Rusu al., 2019)
61.76 + 0.08%
77.59 + 0.12%
ResNet-10
MatchingNet
59.10 0.64%
70.96 + 0.65%
FT
58.76 = 0.61%
72.53 + 0.69%
RelationNet
57.80 0.88%
71.00 0.69%
58.64 = 0.85%
73.78 0.64%
GNN
60.77 - 0.75%
80.87 0.56%
FT
66. 56.32 1 0.80%
81.98 1 0.55%
Table 9: Evaluation with the state-of-the-art approach under the cross-domain setting. We evaluate the metric-based frameworks with the proposed feature-wise transformation layers using pre-determined hyper-parameter {0., 03} {0.3, 0.5} (denoted as FT) against the state-of-the-ar MetaOptNet-SVWN SVM-trainval Lee et al. (2019) method. Note that all the methods are trained only on the mini Mini-ImageNet dataset. To ensure fair comparisons with other methods, we do not the learned version of the feature-wise transformation layers described in Section 3.3. By augmenting the existing metric-based few-shot classification models with the proposed feature-wise transforma- tion layer, we obtain competitive performance when compared with recent and more complicated methods. The best results are highlighted bold.
method
CUB
Cars
Places
Plantac
MetaOpaNeeeSVM--rainna
54.67 + 0.56%
45.90 + 0.49%
65.83 1 0.57%
46.48 1 0.52%
MatchingNet
51.37 1 0.77%
38.99 0.64%
63.16 0.77%
46.53 1 0.68%
FT
55.23 1 0.83%
41.24 1 0.65%
64.55 1 0.75%
41.69 1 0.63%
RelationNet
57.77 = 0.69%
37.33 - 0.68%
63.32 + 0.76%
44.00 = 0.60%
FT
59.46 = 0.71%
39.91 0.69%
66.28 + 0.72%
45.08 = 0.59%
GNN
62.25 = 0.65%
44.28 0.63%
70.84 + 0.65%
52.53 = 0.59%
66.98 + 0.68%
44.90 + 0.64%
73.94 0.67%
53.85 + 0.62%
Comparison to the state-of-the-art few-shot classification under domain shift. We evaluate the metric-based frameworks with the proposed feature-wise transformation layers and the state-of- the-art MetaOptNet approach Lee et al. (2019). We use the model trained on the mini-ImageNet dataset provided by the authors for the evaluation on the other datasets." 6 As shown in Table 9, while the MetaOptNet method achieves state-of-the-an performance on the ImageNet dataset, this approach also suffers from the domain shifts in the cross-domain setting. Training the GNN framework with the pre-trained feature encoder and the proposed feature-wise transformation layers performs favorably against the MetaOptNet method under the cross-domain setting.
thub. hht
18


Reviews:
[{"review_id": "r1x2xr675r", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "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": "In this paper, the authors propose a feature-wise transformation layer for the cross-domain few-shot classification task. Besides, they apply the learning-to-learn procedure to tune the hyperparameters automatically. The primary motivation behind this method does make a lot of sense for me. The reduction of significant shifts in the feature norm could be crucial for successfully transferring the source domain model to the target domain. Also, such a method is method-agnostic, which makes the proposed layer more accessible for all kinds of metric-based query&support pipeline. The extensive experiments for three baselines, MatchingNet, RelationNet, and GNN, indicate the generalizable effectiveness of the proposed layer. Specifically, the visualization and analysis of learned feature space and layer parameters look interesting to me. Remember that the proposed method is designed to minimize the large discrepancy of the feature distribution. Such a study could be beneficial for the reader to understand this paper in depth.\n\nMeanwhile, I have some questions and suggestions for the authors:\n1. I am suggesting the authors include more recent state-of-the-art as baselines and comparisons, which could make such submission much stronger.\n2. This paper delivers an extensive study of the classification problem, how about the other tasks, which heavily rely on classification head, like detection or segmentation. I think it could be more attractive if the author could also show some improvement for these tasks in either a qualitative or quantitive way.\n3. This paper mainly focuses on metric-based few-shot frameworks. At the same time, there are also other two groups, recurrent-based and optimization-based schemes. It could be much better if the authors could also mention the potential of the proposed layer.\n4. This paper leverages the learning-to-learn mechanism to tune the hyper-parameters. How about considering the adaptive or dynamic approach. In other words, build the connection between the theta parameter and image feature.", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "SkeKjHmiYB", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "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": "This paper proposes a feature-wise transformation layer to augment the image features, which is a new regularization of neural networks and leads to better generalization ability of the features. And the proposed method performs well in the few-shot classification problem. Furthermore, the paper develops a learning-to-learn model in the cross-domain setting to choose optimal hyper-parameters of the feature-wise transformation layers. Which leads to consistent improvement in the cross-domain leave-one-out setting.\n\nAlthough I vote weak accept for this paper, I still have several concerns:\n* In equation $(7)$, how to calculate the gradient of $L^{pu}$ w.r.t $\\theta_f^t$ in the condition that the $L^{pu}$ is calculated after removing the feature-wise transformation layers from the model? In my understanding, The $L^{pu}$ is not related to $\\theta_f^t$ after the removal of the feature-wise transformation layers.\n* In Section 4.2, why not choose the Prototypical networks? According to the results in (Chen et al., 2019a), it performs much better in the mini-ImageNet to CUB setting.\n* In Section 4.3, what's the initial value of $\\theta_{\\gamma}$ and $\\theta_{\\beta}$? Is the initial value sensitive to the performance?\n* In Section 4.2, actually, you can learn the $\\theta_{\\gamma}$ and $\\theta_{\\beta}$ automatically even in a single domain. For example, use a different batch to update $\\theta_{\\gamma}$ and $\\theta_{\\beta}$. What's the performance under this setting?\n* There is no direct comparison to the state-of-the-art methods.\n\nOverall, the paper is well written and the figures are well illustrated. The experiments show the effectiveness of the proposed feature-wise transformation layers and the learning-to-learning approach. But the above concerns should be addressed.", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly."}, "extra_text": {}}, {"review_id": "Hke1yO9d9B", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "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": "The authors argue that existing metric learning approaches, for few-shot learning, may cause overfitting to the feature distributions encoded only from the seen domains and thus fail to generalize to unseen domains. This is a domain generaliation task under a few-shot learning setting. The authors proposed a so-called feature-wise transformation layer and integrate it into some existing metric-based few-shot learning methods, which helps learn more diverse feature distributions for better generalization ability.\n\nI think this is a pioneer work on few-shot learning for domain generalization, which is quite interesting.\n\nIn terms of experiments, good performance has been shown by using three existing metric-based few-shot learning methods on a number of settings using five image datasets.\n\nBelow list my major concerns:\n1. The authors mentioned that it is intuitive to have feature-wise transformation layers produce more diverse feature distributions, which would lead to better generaliztion ability for domain generalization. However, it is not obvious to me. Why the generalization ability would be improved with more diverse features?\n\n2. In the experiments, feature-wise transformation layers with hyper-parameters empirically (\\theta_\\gamma, \\theta_\\beta) set as (0.3, 0.5) are compared against learning-to-learn version of the proposed method. Why (0.3, 0.5) was chosen? Is there any chance that another set of (\\theta_\\gamma, \\theta_\\beta) may lead to better performance than the learning-to-learn version? It would be much clearer if the authors can provide detailed parameter analysis (e.g. grid search).\n\n3. Algorithm 1 should be quite expensive in time. It is not clear how the algorithm will be terminated. And in general, how many iterations in the algorithm will be until it achieves some convergence?", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This submission addresses the problem of few-shot classification. The proposed solution centers around metric-based models with a core argument that prior work may lead to learned embeddings which are overfit to the few labeled examples available during learning. Thus, when measuring cross-domain performance, the specialization of the original classifier to the initial domain will be apparent through degraded test time (new domain) performance. The authors therefore, study the problem of domain generalization in the few-shot learning scenario. The main algorithmic contribution is the introduction of a feature-wise transformation layer. 

All reviewers suggest to accept this paper. Reviewer 3 says this problem statement is especially novel. Reviewer 1 and 2 had concerns over lack of comparisons with recent state-of-the-art methods. The authors responded with some additional results during the rebuttal phase, which should be included in the final draft. 

Overall the AC recommends acceptance, based on the positive comments and the fact that this paper addresses a sufficiently new problem statement.

Decision: Accept (Spotlight)