Few-shot Image Generation via Adaptation-aware Kernel Modulation
s
Abstract
Few-shot image generation (FSIG) aims to learn to generate new and diverse samples given an extremely limited number of samples from a domain, e.g. , 10 trainingsamples.
Recent work has addressed the problem using transfer learning approach,leveraging a GAN pretrained on a large-scale source domain dataset and adaptingthat model to the target domain based on very limited target domain samples.
Central to recent FSIG methods are knowledge preserving criteria , which aim toselect a subset of source model’s knowledge to be preserved into the adapted model.
However, a major limitation of existing methods is that their knowledge preservingcriteria consider only source domain/source task , and they fail to consider targetdomain/adaptation task in selecting source model’s knowledge, casting doubt ontheir suitability for setups of different proximity between source and target domain.
Our work makes two contributions.
As our first contribution, we re-visit recent
FSIG works and their experiments.
Our important finding is that, under setupswhich assumption of close proximity between source and target domains is relaxed,existing state-of-the-art (SOTA) methods which consider only source domain inknowledge preserving perform no better than a baseline fine-tuning method.
Toaddress the limitation of existing methods, as our second contribution, we proposeadaptation-aware kernel modulation to address general FSIG of different sourcetarget domain proximity.
Extensive experimental results show that the proposedmethod consistently achieves SOTA performance across source/target domains ofdifferent proximity, including challenging setups when source and target domainsare more apart.
Code / reproducibility details are included.
Introduction
Generative Adversarial Networks (GANs) [6, 7, 8] have been applied to a range of importantapplications including image generation [9, 8, 10], image-to-image translation [11, 12], image editing[13, 14] and data augmentation [15, 16, 17].
However, a critical issue is that these GANs oftenrequire large-scale datasets and computationally expensive resources to achieve good performance
[Table]
FSIG with Transfer Learning.
Recent works in FSIG are based on transfer learning approach [20]
[Table]
i
[Table]
e. , leveraging the prior knowledge of a GAN pretrained on a large-scale, diverse source dataset( e
[Table]
g. , FFHQ [9] or ImageNet [21]) and adapting it to a target domain with very limited samples ( e
[Table]
g. ,face paintings [22]).
As only very limited samples are provided to define the underlying distribution,standard fine-tuning of a pre-trained GAN suffers from mode collapse: the adapted model can onlygenerate samples closely resembling the given few shot target samples [1, 4].
Therefore, recent works[3, 4, 5] have proposed to augment standard fine-tuning with different criteria to carefully preservesubset of source model’s knowledge into the adapted model.
Various criteria has been proposed(Table 1)
[Table]
and these knowledge preserving criteria have been central in recent FSIG research.
Ingeneral, these criteria aim to preserve subset of source model’s knowledge which is deemed to beuseful for target-domain sample generation, e.g. , improving the diversity of target sample generation.
Research Gaps.
One major limitation of existing methods is that they consider only source domain inpreserving subset of source model’s knowledge into the adapted model.
In particular, these methodsfail to consider target domain/adaptation task in selection of source model’s knowledge (Table 1).
For example, EWC [3] applies Fisher Information [23] to select important weights entirely basedon the pretrained source model, and it aims to preserve these selected weights regardless of thetarget domain in adaptation.
Similar to EWC, CDC [4] proposes an additional constraint to preservepairwise distances of generated images by the source model, and there is no consideration of targetdomain/adaptation.
These target/adaptation-agnostic knowledge preserving criteria in recent worksraise question regarding their suitability in different source/target domain setups.
It should be notedthat existing work of FSIG (under very limited target samples) focuses largely on setups where sourceand target domains are in close proximity (semantically) e.g. , Human faces (FFHQ) Ñ Baby faces[3, 5], or Cars Ñ Abandoned Cars [4, 5].
It is unclear about their performance when source/targetdomains are more apart ( e.g. , Human faces (FFHQ) Ñ Animal faces).
Contributions.
In this paper we take an important step to address these research gaps for FSIG.
Specifically, our work makes two contributions.
As our first contribution , we re-visit existingstate-of-the-art (SOTA) algorithms and their experiments.
Importantly, we observe that when theclose proximity assumption is relaxed in experiment setups and source/target domains are moreapart, existing SOTA methods perform no better than a baseline fine-tuning method.
Our observationsuggests that recent methods considering only source domain/source task in knowledge preservingmay not be suitable for general FSIG when source and target domains are more apart.
To validateour claims, we introduce additional experiments with different source/target domains, analyze theirproximity qualitatively and quantitatively, and examine existing methods under a unified framework.
Informed by our analysis, as our second contribution , we propose an adaptation-aware kernelmodulation approach to address general FSIG of different source/target domain proximity.
Inmarked contrast to existing works which preserve knowledge important to source task, our methodaims to preserve subset of source model’s knowledge that are important to the target domain andthe adaptation task.
More specifically, we propose an importance probing algorithm to identifykernels which encode important knowledge for adaptation to the target domain.
Then, we preservethe knowledge of these kernels using a parameter-efficient rank-constrained kernel modulation .
We conduct extensive experiments to show that our proposed method consistently achieves SOTA performance across source/target domains of different proximity, including challenging setups whensource/target domains are more apart.
Our main contributions are summarized as follows:
• We revisit existing FSIG methods and experiment setups.
Our study uncovers issues with
[Figure]
existing methods when applied to source/target domains of different proximity.
[Figure]
• We propose an adaptation-aware kernel modulation method for FSIG.
Our method consis-
[Figure]
tently achieves SOTA performance both visually and quantitatively across source/target
[Figure]
domains with different proximity.
We include our code in Supplementary.
[Figure]
Related Work
Few-shot image generation.
Conventional few-shot learning [24, 25, 26] aims at learning a discriminative classifier for classification [26, 27], segmentation [28, 29] or detection [30, 31] tasks.
Differently, few-shot image generation (FSIG) [4, 3, 5] aims at learning a generator for new anddiverse samples given extremely limited samples ( e.g. , 10 shots).
Transfer learning has been appliedto FSIG.
For example, Transferring GAN [1] ( TGAN ) applies simple GAN loss [6] to fine-tune allparameters of both the generator and the discriminator.
FreezeD [2] fixes a few high-resolution discriminator layers during fine-tuning.
To augment and improve simple fine-tuning, more recent workshave focused on preserving specific knowledge from the source models.
Elastic weight consolidation( EWC ) [3] identifies important weights for the source model and tries to preserve these weights.
Cross-domain Correspondence ( CDC ) [4] preserves pair-wise distance of generated images fromthe source model to alleviate mode collapse.
Dual Contrastive Learning ( DCL ) [5] applies mutualinformation maximization to preserve multi-level diversity of the generated images by the sourcemodel.
In this work, we observe that these SOTA methods perform poorly when source and targetdomains are more apart.
Therefore, their proposed source knowledge preservation criteria may not begeneralizable.
Based on our analysis, we propose an adaptation-aware knowledge selection which ismore generalizable for source/target domains with different proximity.
Revisiting FSIG through the Lens of Source–Target Domain Proximity
In this section, we revisit existing FSIG methods (10-shot) [1, 2, 3, 4, 5] through the lens of sourcetarget domain proximity.
Specifically, we scrutinize the experimental setups of existing FSIG methodsand observe that SOTA [3, 4, 5] largely focus on adapting to target domains that are (semantically)
FFHQ
Babies
Sunglasses
MetFaces
Cat
Dog
Wild
Target Domain
Size
FID Ó LPIPS Ó
FFHQ [32] 70.0K - -
Babies [32] 2.49K 0.
Sunglasses [32] 2.68K 0.
MetFaces [33] 1.33K 0.
Cat [10] 5.15K 0.
Dog [10] 4.74K 0.
Wild [10] 4.74K 0.
proximal to the source domain: Human Faces (FFHQ) Ñ Baby Faces; Human Faces (FFHQ)
Ñ
Sunglasses; Cars Ñ Abandoned Cars; Church Ñ Haunted Houses [3, 4, 5].
This raises the questionas to whether existing source-target domain setups sufficiently represent general FSIG scenarios.
Particularly, real-world FSIG applications may not contain target domains that are always proximal tothe source domain ( e.g. ,: Human Faces (FFHQ) Ñ Animal Faces).
Motivated by this, we conduct anin-depth qualitative and quantitative analysis on source-target domain proximity where we introducetarget domains that are distant from the source domain (Sec 3.1).
Our analysis uncovers an importantfinding: Under our additional setups where the assumption of close proximity between sourceand target domain is relaxed, existing SOTA FSIG methods [3, 4, 5] which consider only sourcedomain/source task in knowledge preserving perform no better than a baseline fine-tuningmethod.
We show this is due to the strong focus of existing SOTA methods in preserving sourcedomain knowledge, thereby not being able to adapt well to distant target domains (Sec 3.2) .
3.1 Source–Target Domain Proximity Analysis
Introducing target domains with varying degrees of proximity to the source domain.
In thissection, we formally introduce source-target domain proximity with in-depth analysis to scrutinizeexisting FSIG methods under different degrees of source-target domain proximity.
Following prior
FSIG works [1, 2, 3, 4, 5], we use FFHQ [8] as the source domain in this analysis.
We remark thatexisting works largely consider different types of human faces as target domain ( i.e. : Babies [8],
Sunglasses [8], MetFaces [33]), To relax close the proximity assumption and study general FSIGproblems, we introduce more distant target domains namely Cat, Dog and Wild (from AFHQ [10],consisting of 15,000 high-quality animal face images at 512 × 512 resolution) for our analysis.
Characterizing source-target domain proximity.
Given the wide success of deep neural networkfeatures in representing meaningful semantic concepts [35, 38, 39, 40], we visualize Inception-v3and LPIPS features for source and target domains to qualitatively characterize domain proximity.
Further, we use FID and LPIPS distance to quantitatively characterize source-target domain proximity.
We remark that FID involves distribution estimation (first, second order moments) [41] and LPIPScomputes pairwise distances (learned embeddings) [35] between source / target domains.
Analysis.
Feature Visualization and FID/ LPIPS measurement results are shown in Figure 2.
Ourresults both qualitatively (columns 1, 2) and quantitatively (column 3) show that target domains usedin existing works (Babies [8], Sunglasses [8], MetFaces [33]) are notably proximal to the sourcedomain (FFHQ), and our additionally introduced target domains (Dog, Cat and Wild [10]) are distantfrom the source domain thereby relaxing the close proximity assumption in existing FSIG works.
3.2 FSIG methods under Relaxation of Close Domain Proximity Assumption
Motivated by our analysis in Section 3.1, we investigate the performance of existing FSIG methods[1, 2, 3, 4, 5] by relaxing the close proximity assumption between source and target domains.
Weinvestigate the performance of these FSIG methods across target domains of different proximity to thesource domain, which includes our additionally introduced target domains: Dog, Cat and Wild.
The
FID results for FFHQ Ñ Cat are: TGAN (simple fine-tuning)
[1]: 64.68, EWC [3]: 74.61, CDC [4]:176.21, DCL [5]: 156.82.
Full results can be found in Table 2.
We emphasize that our investigationuncovers an important finding: Under setups which the assumption of close proximity between sourceand target domain is relaxed (Dog, Cat, Wild), existing SOTA FSIG methods [3, 4, 5] perform nobetter than a baseline fine-tuning method.
This can be consistently observed in Table 2.
This finding is critical as it exposes a serious drawback of SOTA FSIG methods [3, 4, 5] whenclose domain proximity (between source and target) assumption is relaxed.
We further analysegenerated images from SOTA FSIG methods and observe that these methods are unable to adapt wellto distant target domains due to only considering source domain / task in knowledge preservation.
CDC EWC DCL
Ours
G s zzz
CDC
EWC
DCL
G s z 1 z 2 z 3 zz
CDC EWC
[Figure]
This can be clearly observed from Figure 3.
We remark that TGAN (simple baseline) [1] also suffers from severemode collapse.
Given that our investigation uncovers animportant problem in SOTA FSIG methods, we tackle thisproblem in Sec 4.
Figure 3 (last row) shows a glimpse ofour proposed method.
Adaptation-Aware Kernel Modulation
We focus on this question: “Given a pretrained GAN ona source domain D s , and a few-samples from a targetdomain D t , which part of the source model’s knowledgeshould be preserved, and which part should be updated,during the adaptation from D s to D t ?”
In contrast to
SOTA FSIG methods [3, 4, 5], we propose an adaptationaware FSIG that also considers the target domain / adaptation task in deciding which part of the source model’sknowledge to be preserved.
In a CNN, each kernel is responsible for a specific part of knowledge ( e.g. , patternor texture).
Similar behaviour is also observed for bothgenerator [42] and discriminator [43] in GANs.
Therefore, in this work, we make this knowledge preservationdecision at the kernel level, i.e. , casting the knowledgepreservation to a decision problem of whether a kernel isimportant when adapting from D s to D t .
Our FSIG algorithm has two main steps: (i) a lightweight importance probing step, and (ii) mainadaptation step.
In the first step, i.e. , importance probing, we adapt the model using a parameterefficient design to the target domain for a limited number of iterations, and during this adaptation, wemeasure the importance of each individual kernel for the target domain .
The output of importanceprobing are decisions of importance / unimportance of individual kernels.
Then, in the second step,i.e. , main adaptation, we preserve the knowledge of the important kernels and update the knowledgeof the unimportant kernels.
The overview of the proposed system is shown in Figure 1.
Proposed Importance Probing for FSIG.
Our intuition for the proposed importance probing is:“The source GAN kernels have different levels of importance for each target domain.”
For example,different subsets of kernels could be important when adapting a pretrained GAN on FFHQ to
Babies [32] compared to adapting the same GAN to Cat [10].
Therefore, we aim for a knowledgepreservation criteria that is target domain/adaptation-aware (Table 1).
In order to achieve adaptationawareness, we propose a light-weight importance probing algorithm which considers adaptation fromsource to target domain.
There are two important design considerations: probing under (i) extremelylimited number of target data and (ii) low computation overhead.
As discussed, in this importance probing step, we adapt the source model to the target domain for alimited number of iterations and with a few available target samples.
During this short adaptationstep, we measure the importance of kernel for the adaptation task.
To measure the importance, we use
Fisher information (FI) which gives the informative knowledge of that kernel in handling adaptationtask [44].
Then, based on FI measurement, we classify kernels into important / unimportant.
Thesekernel-level importance decisions are then used in the next step, i.e. , main adaptation.
In the main adaptation step, we propose to apply kernel modulation to achieve restrained updatefor the important kernels, and simple fine-tuning for the unimportant kernels.
As will be discussed,the modulation is rank-constrained and has restricted degree-of-freedom; therefore, it is capable topreserve knowledge of the important kernels.
On the other hand, simple fine-tuning has large degreeof-freedom for updating knowledge of the unimportant kernels.
Furthermore, the rank-constrainedkernel modulation is parameter-efficient.
Therefore, we also apply this rank-constrained kernelmodulation in the probing step to determine the importance of kernels.
Kernel Modulation.
The kernel modulation is used in the main adaptation step to preserve knowledgeof important kernels into the adapted model.
Furthermore, it is also used in the probing step as aparameter-efficient technique to determine importance of kernels.
Specifically, we apply Kernel
ModuLation (KML) which is proposed very recently [27].
In [27], KML is proposed for multimodalfew-shot classification (FSC).
In particular, in [27], KML has been found to be effective for knowledgetransfer between different classification tasks of different modes under few-shot constraint.
Therefore,in our work, we apply KML for knowledge transfer between different generation tasks of differentdomains under limited target domain samples.
Specifically, in each convolutional layer of a CNN, the i th kernel of that layer W i P R c in ˆ k ˆ k isconvolved with the input feature X P R c in ˆ h ˆ w to the layer to produce the i th output channel (featuremap)
Y i P
R h
ˆ w 1 , i.e. , Y i
“ W i ˚ X ` b i , where b i P R denotes the bias term.
Then, KML [27]modulates W i by multiplying it with the modulation matrix M i P R c in ˆ k ˆ k plus an all-ones matrix
J P R c in ˆ k ˆ k :
[Equation]
where d denotes the Hadamard multiplication.
In Eqn. 1, using J allows to learn the modulationmatrix in a residual format.
Therefore, the modulation weights are learned as perturbations aroundthe pretrained kernels which helps to preserve source knowledge.
The exact pretrained kernel canalso be transferred to the target model if it is optimal.
There are some important differences betweendiscriminative version of KML in [27] and our version, please see Supplementary for details.
This baseline KML learns an individual modulation parameter for each coefficient of the kernel.
Therefore, it could suffer from parameter explosion when using in recent GAN architectures ( e.g. ,more than 58M parameters in StyleGAN-V2 [8] 1 ).
To address this issue, instead of learning themodulation matrix, we learn a low-rank version of it [27, 45].
More specifically, for a Conv layerwithin CNN, with a total number of d out kernels to be modulated, instead of learning M “ t M i u
d out i “ 1 ,we learn two proxy vectors m 1 P R d out , and m 2 P R p c in ˆ k ˆ k q , and construct the modulation matrixusing the outer product of these vectors, i.e. , M “ m 1 b m 2 .
Furthermore, as we are using KMLfor adaptable knowledge preservation, we freeze the base kernel W i during adaptation.
Therefore,trainable parameters are m 1 , m 2 .
This reduces the number of trainable parameters significantly, andhas better performance on restraining the update of important kernels (see Supplementary).
As it will be discussed later, the value of d out equals to the total number of kernels in a layer ( c out ) duringprobing, and for main adaptation, it is determined by the output of our probing method ( d out ď c out ).
Importance Measurement.
Recall our FSIG has two main steps: (i) importance probing step, and(ii) main adaptation step.
In probing, we also apply KML as a parameter-efficient technique todetermine importance of individual kernels.
In particular, for probing, we propose to apply KMLto all kernels (in both generator and discriminator) to identify which of the modulated kernels areimportant for the adaptation task.
To measure the importance of the modulated kernels, we apply
Fisher information (FI) to the modulation parameters.
In our FSIG setup, for a modulated GAN withparameters Θ , Fisher information F can be computed as:
[Equation]
where L p x | Θ q is the binary cross-entropy loss computed using the output of the discriminator, and xincludes few-shot target samples, and fake samples generated by GAN.
Then, FI for a modulationmatrix F p M i q can be computed by averaging over FI values of parameters within that matrix.
As weare using the low-rank estimation to construct the modulation matrix, we can estimate F p M i q by FIvalues of the proxy vectors.
In particular, considering the outer product in low-rank approximation,we have M i “ reshape pr m 1 p i q m 2 p 1 q , . . .
, m [-i 
-]1 p
i q m 2 p k qsq , where k “ | m 2 | .
Then we use theunweighted average of FI for parameters of m 1 and m 2 , proportional to their occurrence frequencyin calculation of M i , as an estimate of F p M i q (details in Supplementary):
[Equation]
After calculating ˆ F p M i q for all modulation matrices in both generator and discriminator, we usethe t % quantile of these values as a threshold (separately for generator and discriminator) to decidewhether modulation of a kernel is important or unimportant for adaptation to the target domain.
If themodulation of a kernel is determined to be important (during probing), the kernel is modulated using
KML during main adaptation step; otherwise, the kernel is updated using simple fine-tuning duringmain adaptation.
In all setups, we perform probing for 500 iterations and use a fixed t “ 75 .
Weremark that in probing only modulation parameters m 1 , m 2 are trainable, and FI is only computedon them, the probing is a very lightweight step and can be performed with minimal overhead (detailsin Supplementary).
The output of probing step are the decisions to apply kernel modulation or simplefine-tuning on individual kernels.
Then, based on these decisions, the main adaptation is performed.
Empirical Studies
5.1 Experiments / Results
Experiment Details.
For fair comparison, we strictly follow prior works [1, 2, 3, 4, 5] in the choiceof GAN architecture, source-target adaptation setups and hyper-parameters.
We use StyleGAN-V2[8] as the GAN architecture and FFHQ as the source domain.
Our experiments include setups withdifferent source-target proximity: Babies/Sunglasses [4], MetFaces [33] and Cat/Dog/Wild (AFHQ)[10] (See Sec. 3).
We use 256 x 256 resolution for adaptation.
Adaptation is performed with batchsize 4 on a single Tesla V100 GPU.
We apply importance probing and modulation on base kernels ofboth generator and discriminator.
We focus on 10-shot target adaptation setup in the main paper.
Qualitative Results.
We show generated images with our proposed adaptation-aware FSIG methodalong Baseline [1, 2] and SOTA FSIG methods [3, 4, 5] for 2 target domains, Babies and Cats withdifferent degrees of proximity to FFHQ, before and after adaptation.
The results are shown in Figure4 top and bottom respectively.
By preserving source domain knowledge that is important for targetdomain, our proposed adaptation-aware FSIG method can generate substantially high quality imageswith high diversity for both Babies and Cat domains.
We also include FID and Intra-LPIPS metrics toquantitatively show that our proposed method outperforms SOTA FSIG methods [3, 4, 5].
We showmore generated samples in Supplementary.
[Figure]
Babies
TGAN
FreezeD
CDC
EWC
FID ( ↓ ) Intra-LPIPS ( ↑ )
DCL 101.
48.83 Ours 0.
G s (Pretrained) 64.
10-Shot Real Cats
G s
TGAN
FreezeD
CDC
EWC
DCL
Ours
FID ( ↓ ) Intra-LPIPS ( ↑ ) (Pretrained)
Quantitative Results.
We show complete FID scores in Table 2.
Our proposed adaptation-aware
FSIG achieves SOTA results across all target domains of varying proximity to the source (FFHQ).
We emphasize that it is achieved by preserving source domain knowledge that is important for targetdomain adaptation (Sec 4).
We also report Intra-LPIPS ( Ò ) as an indicator of diversity, as Figure 4.
5.2 Analysis
Ablation study of Importance Probing.
The goal of importance probing (denoted as “IP”) is toidentify kernels that are important for few-shot target adaptation as shown in Figure 5 (Top).
Tojustify the effectiveness of our design choice, we perform an ablation study that discards the IP stageand regard all kernels as equally important for target adaptation.
Therefore, we simply modulate allkernels without any knowledge selection .
As one can observe from Figure 5 (Bottom), knowledgeselection plays a vital role in adaptation performance.
Specifically, the significance of knowledgepreservation is more evident when the target domains are distant from the source domain.
Number of target samples (shots).
The number of target domain training samples is an importantfactor that can impact the FSIG performance.
In general, more target domain samples can allowbetter estimation of target distribution.
We study the efficacy of our proposed method under different
[Table]
[Figure]
number of target domain samples.
The results are shown in Figure 6, and we show that our proposedadaptation-aware FSIG method consistently outperforms existing FSIG methods in all setups.
Discussion
Conclusions.
Focusing on FSIG, we make two contributions.
First, we re-visit current SOTAmethods and their experiments.
We discover that SOTA performs poorly in setups when sourceand target domains are more distant, as existing methods consider only source domain/task forknowledge preservation.
Second, we propose a new FSIG method which is target/adaptation-aware.
Our proposed method outperforms previous work across all setups of different source-target domainproximity.
Broader Impact.
Our work makes contribution to generation of synthetic data in applications wheresample collection is challenging, e.g., photos of rare animal species.
This is an important contributionto many data-centric applications.
Furthermore, transfer learning of generative models using a fewdata sample enables data and computation-efficient model development.
Our work has positiveimpact on environmental sustainability and reduction of greenhouse gas emission.
Limitations.
While our experiments are extensive compared to previous works, in practical applications, there are many possible target domains which cannot be included in our experiments.
However
as method
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