Title: WHEN DOES SELF-SUPERVISION IMPROVE FEW-SHOT LEARNING?

Abstract: We present a technique to improve the generalization of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions. Although recent research has shown benefits of self-supervised learning (SSL) on large unlabeled datasets, its utility on small datasets is unknown. We find that SSL reduces the relative error rate of few-shot meta-learners by 4%-27%, even when the datasets are small and only utilizing images within the datasets. The improvements are greater when the training set is smaller or the task is more challenging. Though the benefits of SSL may increase with larger training sets, we observe that SSL can have a negative impact on performance when there is a domain shift between distribution of images used for meta-learning and SSL. Based on this analysis we present a technique that automatically select images for SSL from a large, generic pool of unlabeled images for a given dataset using a domain classifier that provides further improvements. We present results using several meta-learners and self-supervised tasks across datasets with varying degrees of domain shifts and label sizes to characterize the effectiveness of SSL for few-shot learning.

1. INTRODUCTION:

Current machine learning algorithms require enormous amounts of training data to learn new tasks. This is a problem for many practical problems across domains such as biology and medicine where labeled data is hard to come by. In contrast, we humans can quickly learn new concepts from limited training data. We are able to do this by relying on our past "visual experience". Recent work attempts to emulate this by training a feature representation to classify a training dataset of "base" classes with the hope that the resulting representation generalizes not just to unseen examples of the same classes but also to novel classes, which may have very few training examples (called fewshot learning). However, training for base class classification can force the network to only encode features that are useful for distinguishing between base classes. In the process, it might discard semantic information that is irrelevant for base classes but critical for novel classes. This might be especially true when the base dataset is small or when the class distinctions are challenging.

One way to recover this useful semantic information is to leverage representation learning techniques that do not use class labels, namely, unsupervised or self-supervised learning. The key idea is to learn about statistical regularities (e.g., spatial relationship between patches, orientation of an images) that might be a cue to semantics. Despite recent advances, these techniques have only been applied to a few domains (e.g., entry-level classes on internet imagery), and under the assumption that large amounts of unlabeled images are available. Their applicability to the general few-shot scenario described above is unclear. In particular, can these techniques help prevent overfitting to base classes and improve performance on novel classes in the few-shot setting? If so, does the benefit generalize across domains and to more challenging tasks? Moreover, can we use self-supervised training to boost performance in domains where even unlabeled images are hard to get? This paper seeks to answer these questions. We show that with no additional training data, adding a self-supervised task as an auxiliary task (Figure 1 ) improves the performance of existing few-shot techniques on benchmarks across a multitude of domains (Figure 2 ). Intriguingly, we find that the benefits of self-supervision increase with the difficulty of the task, for example when training from a smaller base dataset, or with degraded inputs such as low resolution or greyscale images (Figure 3 ).

One might surmise that as with traditional SSL, additional unlabeled images might improve performance further. But what unlabeled images should we use for novel problem domains where unlabeled data is not freely available? To answer this, we conduct a series of experiments with additional unlabeled data from different domains. We find that adding more unlabeled images improves performance only when the images used for self-supervision are within the same domain as the base classes (Figure 4a ); otherwise they can even negatively impact the performance of the few-shot learner (Figure 4b, 4c, 4d ). Based on this analysis we present a simple approach that uses a domain classifier to pick similar-domain unlabeled images for self-supervision from a large, generic pool of images. The resulting method improves over the performance of a model trained with self-supervised learning from images within the domain (Figure 4c ). Taken together, this results in a powerful, general and practical approach for improving few-shot learning from small datasets in novel domains. Finally, these benefits are also observed on standard classification tasks (Appendix A.3).

2. RELATED WORK:

Few-shot learning Few-shot learning aims to learn representations that generalize well to the novel classes where only few images are available. To this end several meta-learning approaches have been proposed that evaluate representations by sampling many few-shot tasks within the domain of a base dataset. These include optimization-based meta-learners, such as model-agnostic meta-learner (MAML) (Finn et al., 2017) , gradient unrolling (Ravi & Larochelle, 2017) , closed form solvers (Bertinetto et al., 2019) , convex learners (Lee et al., 2019) , etc. Others such as matching networks (Vinyals et al., 2016) and prototypical networks (Snell et al., 2017) rely on distance based classifiers, while Garcia & Bruna (2018) models higher-order relationships in data using a graph network. Another class of methods (Gidaris & Komodakis, 2018; Qi et al., 2018; Qiao et al., 2018) model the mapping between training data and classifier weights using a feed-forward network.

While the literature is rapidly growing, a recent study by Chen et al. (2019) has shown that the differences between meta-learners are diminished when deeper network architectures are used and they develop a strong baseline for few-shot learning based on ResNet18 architecture and prototypical networks Snell et al. (2017) . We build our experiments on top of this work and show that auxiliary self-supervised tasks provide additional benefits with state-of-the-art deep networks across several few-shot benchmarks. We also find that prototypical networks are often the best performing, though the benefits of SSL generalize across meta-learners such as MAML.

Self-supervised learning Human labels are expensive to collect and hard to scale up. To this end, there has been increasing research interest to investigate learning representations from unlabeled data. In particular, the image itself already contains structural information which can be utilized. Self-supervised learning approaches attempt to capture this.

There is a rich line of work on self-supervised learning. One class of methods removes part of the visual data, and tasks the network with predicting what has been removed from the rest in a discriminative manner (Larsson et al., 2016; Zhang et al., 2016; 2017) . Doersch et al. (2015) proposed to predict the relative position of patches cropped from images. Wang & Gupta (2015) used the similarity of patches obtained from tracking as a self-supervised task and combined position and similarity predictions of patches (Wang et al., 2017) The focus of most prior works on self-supervised learning is to supplant traditional fully supervised representation learning with unsupervised learning on large unlabeled datasets for downstream tasks. Crucially in almost all prior works, self-supervised representations consistently lag behind fullysupervised representations trained on the same dataset with the same architecture (Goyal et al., 2019; Kolesnikov et al., 2019) . In contrast, our work focuses on an important counterexample: self- Multi-task learning Our work is related to multi-task learning, a class of techniques that train on multiple task objectives together to improve each one. Previous works in the computer vision literature have shown moderate benefits by combining tasks such as edge, normal, and saliency estimation for images, or part segmentation and detection for humans (Kokkinos, 2017; Maninis et al., 2019; Ren & Lee, 2018) . However, there is significant evidence that training on multiple tasks together often hurts performance on individual tasks (Kokkinos, 2017; Maninis et al., 2019) . Only certain task combinations appear to be mutually beneficial, and sometimes specific architectures are needed. Our key contribution here is showing that self-supervised tasks and few-shot learning are indeed mutually beneficial in this sense. The fact that self-supervised tasks require no annotations makes our finding especially practically significant.

Concurrent works:

At the time of submission several concurrent works appear to draw similar conclusions to ours. We briefly describe these, deferring a detailed comparison to the future when code and experimental details become available. Asano et al. (2019) finds that self-supervised pretraining is useful even with a single image. However, very large images and extreme data augmentations are used. Carlucci et al. (2019) uses self-supervision to improve domain generalization. Gidaris et al. (2019), the most related work to ours, also shows that self-supervision improves few-shot learning. Although this initial result is the same, we further show these benefits on smaller datasets with harder recognition problems (fine-grained classification) and with deeper models. Moreover, we also present a novel analysis of the impact of the domain of unlabeled data, and a new, simple approach to automatically select similar-domain unlabeled data for self-supervision.

3. METHOD:

We adopt the commonly used setup for few-shot learning where one is provided with labeled training data for a set of base classes D b and a much smaller training set (typically 1-5 examples per class) for novel classes D n . The goal of the few-shot learner is to learn representations on the base classes that lead to good generalization on novel classes. Although in theory the base classes are assumed to have a large number of labeled examples, in practice this number can be quite small for novel domains, e.g. less than 5000 examples for the birds dataset (Welinder et al., 2010) , making it challenging to learn a generalizable representation.

Our framework, as seen in Figure 1 , combines meta-learning approaches for few-shot learning that rely on supervised labels with self-supervised learning on unlabeled images. Denote a training dataset as {(x i , y i )} n i=1 consisting of pairs of images x i ∈ X and labels y i ∈ Y. A feed-forward convolutional network f (x) maps the input to an embedding space, which is then mapped to the label space using a classifier g. The overall mapping from the input to the label can be written as g • f (x) : X → Y. Learning consists of estimating functions f and g that minimize a loss on the predicted labels on the training data along with suitable regularization R over the functions f and g. This can be written as:

L s := i g • f (x i ), y i + R(f, g).

A commonly used loss is the cross-entropy loss and a regularizer is the 2 norm of the parameters of the functions. For transfer g is discarded and relearned on training data for novel classes.

In addition, we consider self-supervised losses L ss based on labeled data x → (x, ŷ) that can be derived from inputs x alone. Figure 1 shows two examples: the jigsaw task rearranges the input image and uses the index of the permutation as the target label, while the rotation task uses the angle of the rotated image as the target label. In our framework, a separate function h is used to predict these labels from the shared feature backbone f with a self-supervised loss:

L ss := i h • f (x i ), ŷi .

Our final loss function combines the two losses: L := L s + L ss , and thus the self-supervised losses act as a data-dependent regularizer for representation learning. Note that the domain of images used for supervised and self-supervised losses denoted by D s and D ss need not be identical. In particular we often would like to use significantly larger sets of images for self-supervised learning from related domains. Below we describe various losses we consider for image classification tasks. Prototypical networks are related to distance-based learners such as matching networks (Vinyals et al., 2016) or metric-learning based on label similarity (Koch et al., 2015) . In the Appendix we also present few-shot classification results using a gradient-based meta-learner called MAML (Finn et al., 2017) , and one trained with a standard cross-entropy loss on the base classes. We also present standard classification results where the test set contains images from the same base categories.

3.2. SELF-SUPERVISED LOSSES (L ss ):

We consider two losses motivated by a recent large-scale comparison of the effectiveness of selfsupervised learning tasks (Goyal et al., 2019) described below:

Jigsaw puzzle task loss. In this case the input image x is tiled into 3×3 regions and permuted randomly to obtain an input x. The target label ŷ is the index of the permutation. The index (one of 9!) is reduced to one of 35 following the procedure outlined in Noroozi & Favaro (2016), which grouped the possible permutations based on hamming distance to control the difficulty of the task.

Rotation task loss. We follow the method of Gidaris et al. (2018) where the input image x is rotated by an angle θ ∈ {0°, 90°, 180°, 270°} to obtain x and the target label ŷ is the index of the angle.

In both cases we use the cross-entropy loss between the target and prediction.

3.3. STOCHASTIC SAMPLING AND TRAINING:

When the source of images used for SSL and meta-learning are identical the same batch of images are used for computing L s and L ss . For experiments investigating domain shifts (Sec. We use the original splits for mini-ImageNet and tiered-ImageNet.

We also present results on a setting where the base set is ) with a learning rate of 0.001 for 60,000 episodes. We report the mean accuracy and 95% confidence interval over 600 test experiments. In each test episode, N classes are selected from the novel set, and for each class 5 support images and 16 query images (or 5 query images when using 20% labeled data) are used. We report results for N = {5, 20} classes.

Image sampling and data augmentation Data augmentation has a significant impact on few-shot learning performance. We follow the data augmentation procedure outlined in Chen et al. ( 2019) which resulted in a strong baseline performance of meta-learning. For label and rotation predictions, images are first resized to 224 pixels for the shorter edge while maintaining the aspect ratio, from which a central crop of 224×224 is obtained. For jigsaw puzzles, we first randomly crop 255×255 region from the original image with random scaling between [0.5, 1.0], then split into 3×3 regions, from which a random crop of size 64×64 is picked. While it might appear that with self-supervision the model effectively sees more images, we wish to point out that all the experiments use significant data augmentation, including cropping, flipping, and color jittering, which also significantly increases the amount of training data. The gains from the self-supervised auxiliary tasks are over and above any gains from data augmentation alone. More experimental details are in Appendix A.5.

Other experimental results:

In Appendix A.3, we show the benefits of using self-supervision for standard classification tasks when training the model from scratch. We further visualize these models in Appendix A.4 to show that models trained with self-supervision tend to avoid accidental correlation of background features to class labels. Figure 3 : Benefits of SSL for harder few-shot learning tasks. We show the accuracy of using jigsaw puzzle task over ProtoNet baseline on harder versions of the datasets. We see that SSL is effective even on smaller datasets and the relative benefits are higher.

mini-ImageNet tiered-ImageNet:



4.1. RESULTS ON FEW-SHOT LEARNING:

Self-supervised learning improves few-shot learning Figure 2 shows the accuracies of various models for 5-way 5-shot classification on few-shot benchmarks. Our ProtoNet baseline matches the results of the mini-ImageNet and birds datasets presented in Chen et al. (2019) (in their Table A5 ). Our results show that jigsaw puzzle task improves the ProtoNet baseline on all seven datasets. Specifically, it reduces the relative error rate by 4.0%, 8.7%, 19.7%, 8.4%, 4.7%, 15.9%, and 27.8% on mini-ImageNet, tiered-ImageNet, birds, cars, aircrafts, dogs, and flowers datasets respectively. Predicting rotations also improves the ProtoNet baseline on most of the datasets, except for aircrafts and flowers. We speculate this is because most flower images are symmetrical, and airplanes are usually horizontal, making the rotation task too hard or too trivial to benefit main task. In addition, combining these two SSL tasks can be beneficial sometimes. Our results also echo those of Gidaris et al. ( 2019) who find that the rotation task improves on miniand tiered-ImageNet. A tabular version of the figure and the results for 20-way classification are included in Table 2 in Appendix A.2.

Gains are larger for harder tasks Figure 3 shows the performance on the degraded version of the same datasets (first five groups). For cars and aircrafts we use low-resolution images where the images are down-sampled by a factor of four and up-sampled back to 224×224 with bilinear interpolation. For natural categories we discard color. Low-resolution images are considerably harder to classify for man-made categories while color information is most useful for natural categories (Su & Maji, 2017) . On birds and dogs datasets, the improvements using self-supervision (3.2% and 2.9% on 5-way 5-shot) are higher compared to color images (2.5% and 2.7%), similarly on the cars and aircrafts datasets with low-resolution images (2.2% and 2.1% vs. 0.7% and 0.4%). We also conduct an experiment where only 20% of the images in the base categories are used for both SSL and meta-learning (last five groups in Figure 3 ). This results in a much smaller training set than standard few-shot benchmarks: 20% of the birds dataset amounts to only roughly 3% of the popular mini-ImageNet dataset. We find larger benefits from SSL in this setting. For example, the gain from the jigsaw puzzle loss for 5-way 5-shot car classification increases from 0.7% (original dataset) to 7.0% (20% training data). A comparison of approaches to sample images from a pool for SSL. With random sampling, the extra unlabeled data often hurts the performance, while those sampled using the "domain weighs" improves performance on most datasets. When the pool contains images from related domains, e.g., birds and dogs, the performance using selected images matches that of using 100% within-dataset images. (d) Accuracy of the meta-learner with SSL on different domains as function of distance between the supervised domain D s and the self-supervised domain D ss . See text for details.

Improvements generalize to other meta-learners We combine SSL with other meta-learners and find the combination to be effective. In particular we use MAML (Finn et al., 2017 ) and a standard feature extractor trained with cross-entropy loss (softmax). We observe that the average 5-way 5shot accuracies across five fine-grained datasets for softmax, MAML, and ProtoNet improve from 85.5%, 82.6% and 88.5% to 86.6%, 83.8% and 90.4% respectively when combined with the jigsaw puzzle task. Detailed results are shown in Table 4 in Appendix A.2.

Self-supervision alone is not enough SSL alone significantly lags behind supervised learning in our experiments. For example, a ResNet18 trained with SSL alone achieve 32.9% (w/ jigsaw) and 33.7% (w/ rotation) 5-way 5-shot accuracy averaged across five fine-grained datasets. While this is better than a random initialization (29.5%), it is dramatically worse than one trained with a simple cross-entropy loss (85.5%) on the labels (Details in Table 2 ). Surprisingly, we found that initialization with SSL followed by meta-learning did not yield improvements over meta-learning starting from random initialization, supporting the view that SSL acts as a feature regularizer.

4.2. SELECTING DOMAINS FOR SELF-SUPERVISION:

A promising direction of research in SSL is by scaling it to massive unlabeled datasets which are readily available for some domains. However, does more unlabeled data always help for a task in hand? This question hasn't been sufficiently addressed in the literature as most prior works study the effectiveness of SSL on a curated set of images, such as ImageNet, and their transferability to a handful of tasks. In this section we conduct a series of experiments to characterize effect of size and distribution D ss of images used for SSL in the context of few-shot learning on a domain D s .

First we investigate if SSL on unlabeled data from the same domain improves the meta-learner. We use 20% of the images in the base categories for meta-learning identical to setting in Figure 3 . The labels of the remaining 80% data are withheld and only the images are used for SSL. We systematically vary the number of images used by the SSL from 20% to 100%. Results are presented in Figure 4a . The accuracy improves with the size of the unlabeled set with diminishing returns. Note that 0% corresponds to no SSL and 20% corresponds to using only the labeled images for SSL.

Figure 4b shows an experiment where a fraction of the unlabeled images are replaced with images from other datasets. For example 20% along the x-axis for birds indicate that 20% of the images in base set are replaced by images drawn uniformly at random from other datasets. Since the number of images used for SSL are identical, the x-axis from left to right represents increasing amounts of domain shifts between D s and D ss . We observe that the effectiveness of SSL decreases as the fraction of out-of-domain images increases. Importantly, training with SSL on the available 20% within domain images (shown as crosses) is often (3 out of 5 datasets) better than increasing the set of images by five times to include out of domain images.

Figure 4d analyzes how the magnitude of the domain shift affects performance. Here all the images in base classes are used (instead of using 20% data in previous experiment), and all the images for SSL are sampled from different datasets. In addition to the five datasets (birds, cars, aircrafts, dogs, flowers), we include 1) 5000 images of Aves from iNaturalist 2018 dataset (iNat) (Van Horn et al., 2018), 2) 5000 images of other super-classes from iNat, and 3) 5760 images sampled from mini-ImageNet. These domains are roughly of the same sizes (between 5k to 10k images each) but exhibit varying degrees of domain shifts. To compute a "distance" between a pair of domains, we first estimate an embedding for each domain D using the mean image feature:

Φ(D) = xi∈D φ(x i )/N ,

where N is the number of images within D, and use the Euclidean distance between their embeddings. Here φ(x) is an image embedding from a ResNet101 network pretrained on ImageNet.

Figure 4d shows the accuracy of the meta-learner when combined with SSL on different domains as a function of the domain distance between D s and D ss . Once again we see that the effectiveness of SSL decreases with the distance from the supervised domain across all datasets.

Based on the above analysis we propose a simple method to select images for SSL from a large, generic pool of unlabeled images in a dataset dependent manner. We use a "domain weighted" model to select the top images based on a domain classifier, in our case a binary logistic regression model trained with images from the source domain D s as the positive class and from pool D p as the negative class based on ResNet101 image features. The top images are selected according to the ratio p(x ∈ D s )/p(x ∈ D p ). Note that these importance weights account for the domain shift. We evaluate this approach using a pool of images from all five datasets as well as 30000 images each from Aves of iNat, other super-classes of iNat and mini-ImageNet. For each dataset we use 20% of the labeled images for meta-learning, then use the pool in a leave-one-out manner: all images from a dataset are excluded from the pool to simulate a scenario where one does not have additional within-domain images. The compositions of the selected images are shown in Appendix A.6.

Figure 4c shows results across datasets with meta-learner trained on 20% labeled examples. SSL with 20% and 100% dataset denotes baselines when SSL uses 20% and 100% of the images within domain. The latter is a reference "upper bound". SSL pool "(random)" and "(weight)" denote two approaches for selecting the remaining 80% of images from the pool. The former selects images uniformly at random, which is detrimental for cars, dogs, and flowers. For dogs the performance is even worse than no SSL. The pool selected according to the importance weights provides significant improvements on birds and dogs as the pool has relevant images, and outperforms the random selection on most datasets. These results are likely to improve by collecting an even larger pool.

5. CONCLUSION:

We have shown that self-supervision improves transferability of representations on few-shot learning tasks across a range of different domains. Surprisingly, we found that self-supervision is more beneficial for more challenging problems, especially when the number of images used for selfsupervision is small, orders of magnitude smaller than previously reported results. This has a practical benefit that the images within small datasets can be used for self-supervision without relying on a large-scale external dataset. We have also shown that additional unlabeled images can improve performance only if they are from the same or similar domains. Finally, for domains where unlabeled data is limited, we present a novel, simple approach to automatically identify such similar-domain images from a larger pool. 

A.2 RESULTS ON FEW-SHOT LEARNING:

Table 2 shows the performance of ProtoNet with different self-supervision on seven datasets. We also test the accuracy of the model on novel classes when trained only with self-supervision on the base set of images. Compared to the randomly initialized model ("None" rows), training the network to predict rotations gives around 2% to 21% improvements on all datasets, while solving jigsaw puzzles only improves on aircrafts and flowers. However, these numbers are significantly worse than learning with supervised labels on the base set, in line with the current literature.

Table 3 shows the performance of ProtoNet with jigsaw puzzle loss on harder benchmarks. The results on degraded version of the datasets are shown in the top part, and the bottom part shows the results of using only 20% of the images in the base categories. The gains using SSL are higher in this setting. statistics. At test time we do the same. We found the accuracy goes up as the batch size increases but saturates at size of 64.

When training with supervised and self-supervised loss, a trade-off term λ between the losses can be used, thus the total loss is L = (1 -λ)L s + λL ss . We find that simply use λ = 0.5 works the best, except for training on miniand tiered-ImageNet with jigsaw loss, where we set λ = 0.3. We suspect that this is because the variation of the image size and the categories are higher, making the self-supervision harder to train with limited data. When both jigsaw and rotation losses are used, we set λ = 0.5 and the two self-supervised losses are averaged for L ss . Optimization details for standard classification For standard classification (Appendix A.3) we train a ResNet18 network from scratch. All the models are trained with ADAM optimizer with a learning rate of 0.001 for 600 epochs with a batch size of 16. We track the running statistics for the batch normalization layer for the softmax baselines following the conventional setting, i.e. w/o self-supervised loss, but do not track these statistics when training with self-supervision.

Architectures for self-supervised tasks For jigsaw puzzle task, we follow the architecture of Noroozi & Favaro (2016) where it was first proposed. The ResNet18 results in a 512-dimensional feature for each input, and we add a fully-connected (fc) layer with 512-units on top. The nine patches gives nine 512-dimensional feature vectors, which are concatenated. This is followed by a fc layer, projecting the feature vector from 4608 to 4096 dimensions, and a fc layer with 35dimensional outputs corresponding to the 35 permutations for the jigsaw task.

For the rotation task, the 512-dimensional output of ResNet18 is passed through three fc layers with {512, 128, 128, 4} units. The predictions correspond to the four rotation angles. Between each fc layer, a ReLU activation and a dropout layer with dropout probability of 0.5 are added.

A.6 DETAILS OF THE DATASET SELECTED FROM POOL Table 6 describes the composition of the unlabeled images sampled from a generic pool for SSL experiments presented in Section 4.2 (Figure 4c ). "Random sampling" uniformly samples images proportional to the size of each dataset, while the "domain weighted" one tends to pick more images from related domains. This is also indicated by the domain distance of the sampled images with respect to the source domain. The improvements are larger when the domain distance is small, e.g., for birds, dogs, and flowers. Table 6 : Composition of the unlabeled datasets selected from a pool of images used in Figure 4c .

Dataset

We list the number of images in each source datasets. We also provide domain distance between the selected images and the labeled dataset. The 5-way 5-shot accuracy of the models trained with SSL are shown in the last row. The performance correlates with domain distance: when the distribution of unlabeled data is closer to labeled data, the performance is better.

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Figures and Tables:

. Other tasks include predicting rotation (Gidaris et al., 2018), noise (Bojanowski & Joulin, 2017), clusters (Caron et al., 2018; 2019; Wu et al., 2018), number of objects (Noroozi et al., 2017), missing patches (Pathak et al., 2016; Trinh et al., 2019), motion segmentation labels (Pathak et al., 2017), artifacts (Jenni & Favaro, 2018), next patch feature (Oord et al., 2018; Hénaff et al., 2019), or by maximizing mutual information (Hjelm et al., 2019; Bachman et al., 2019), and training an adversarial generative model (Donahue & Simonyan, 2019). Doersch & Zisserman (2017) jointly trained four self-supervised tasks and found it to be beneficial. Recently, Goyal et al. (2019) and Kolesnikov et al. (2019) compared various self-supervised learning tasks at scale and concluded that solving jigsaw puzzles and predicting image rotations are among the most effective, motivating the choice of self-supervised tasks in our experiments.

Figure1: Our framework combines supervised and self-supervised losses to learn representations on small datasets. Self-supervised tasks, such as jigsaw puzzle or rotation prediction, can be derived from the images alone and act as a data-dependent regularizer for the shared feature backbone.

SUPERVISED LOSSES (L s ) Most of our results are presented using a meta-learner based on prototypical networks (Snell et al., 2017) that perform episodic training and testing over sampled datasets in stages called meta-training and meta-testing. During meta-training, we randomly sample N classes from the base set D b , then we select a support set S b with K images per class and another query set Q b with M images per class. We call this an N -way K-shot classification task. The embeddings are trained to predict the labels of the query set Q b conditioned on the support set S b using a nearest mean (prototype) model. The objective is to minimize the prediction loss on the query set. Once training is complete, given the novel dataset D n , class prototypes are recomputed for classification and query examples are classified based on the distances to the class prototypes.

"degraded" either by reducing the resolution, removing color, or the number of training examples to 20% of the original set. This allows us to study the effectiveness of SSL on even smaller datasets and as a function of the difficulty of the task. Further details of the datasets are in the Appendix A.1. Meta-learners and feature backbone We follow the best practices and use the codebase for fewshot learning described in Chen et al. (2019). In particular, we use a prototypical network (Snell et al., 2017) with a ResNet18 (He et al., 2016) as the feature backbone. Their experiments found this to often be the best performing; however, we also experiment our method with other meta-learners such as MAML (Finn et al., 2017) and softmax classifiers. Learning and optimization We use 5-way (classes) and 5-shot (examples per-class) with 16 query images for training. For experiments using 20% of labeled data, we use 5 query images for training since the minimum number of images per class is 10. The models are trained with ADAM (Kingma & Ba, 2015

Effectiveness of SSL as a function of domain distance between Ds and Dss (shown on top).

Figure 4: Effect of size and domain of SSL on 5-way 5-shot accuracy. (a) More unlabeled data from the same domain for SSL improves the performance of the meta-learner. (b) Replacing a fraction (x-axis) of the images with those from other domains makes SSL less effective. (c)A comparison of approaches to sample images from a pool for SSL. With random sampling, the extra unlabeled data often hurts the performance, while those sampled using the "domain weighs" improves performance on most datasets. When the pool contains images from related domains, e.g., birds and dogs, the performance using selected images matches that of using 100% within-dataset images. (d) Accuracy of the meta-learner with SSL on different domains as function of distance between the supervised domain D s and the self-supervised domain D ss . See text for details.

Figure 5: Saliency maps for various images and models. For each image we visualize the magnitude of the gradient with respect to the correct class for models trained with various loss functions. The magnitudes are scaled to the same range for easier visualization. The models trained with selfsupervision often have lower energy on the background regions when there is clutter. We highlight a few examples with blue borders and the bounding-box of the object for each image is shown in red.

training meta-learners, we use 16 query images per class for each training episode. When only 20% of labeled data are used, 5 query images per class are used. For MAML, we use 10 query images and the approximation method for backpropagation as proposed in Chen et al. (2019) to reduce the GPU memory usage. When training with self-supervised loss, it is added when computing the loss in the outer loop. We use PyTorch (Paszke et al., 2017) for our experiments.For the domain classifier, we first obtain features from the penultimate-layer (2048 dimensional) from a ResNet101 model pretrained on ImageNet. We then train a binary logistic regression model with weight decay using LBFGS for 1000 iterations. The images from labeled dataset are the positive class and images from pool are the negative. A loss for the positive class is scaled by the inverse of its frequency to account for the significantly larger number of negative examples.

task, the two losses are combined. While other techniques exist(Chen et al., 2018;Kendall et al., 2018;Sener & Koltun, 2018), simply combining the two losses performed well.4 EXPERIMENTSWe first describe the datasets and experimental details. We then present results for few-shot learning on different datasets and SSL tasks in Section 4.1. Last, we investigate the effectiveness of SSL with unlabeled data sampled from different domains in Section 4.2.Datasets and benchmarksWe experiment with datasets across diverse domains: Caltech-UCSD birds (Welinder et al., 2010), Stanford cars (Krause et al., 2013), FGVC aircrafts (Maji et al., 2013), Stanford dogs (Khosla et al., 2011), and Oxford flowers (Nilsback & Zisserman, 2006). Each dataset contains between 100 to 200 classes with a few thousands of images. We also experiment with the widely-used mini-ImageNet (Vinyals et al., 2016) and tiered-ImageNet (Ren et al., 2018) benchmarks for few-shot learning. In mini-ImageNet, each class has 600 images, where in tiered-ImageNet each class has 732 to 1300 images.We split classes within a dataset into three disjoint sets: base, val, and novel. For each class all the images in the dataset are used in the corresponding set. A model is trained on the base set of categories, validated on the val set, and tested on the novel set of categories given a few examples per class. For birds, we use the same split as Chen et al. (2019), where {base, val, novel} sets have {100, 50, 50} classes respectively. The same ratio is used for the other four fine-grained datasets.

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Benefits of SSL for few-shot learning tasks. We show the accuracy over the ProtoNet baseline of using different SSL tasks. The jigsaw task results in an improvement of the 5-way 5-shot classification accuracy for a ProtoNet across datasets. Combining SSL tasks can be beneficial for some datasets. Here SSL was performed on images within the base classes only. See Table2in the Appendix for a tabular version and results for 20-way 5-shot classification.

Table content: <table><tr><td/><td>ProtoNet</td><td colspan="2">ProtoNet + Jigsaw</td><td colspan="2">ProtoNet + Rotation</td><td>ProtoNet + Jigsaw + Rotation</td></tr><tr><td/><td>95</td><td/><td/><td/></tr><tr><td>Accuracy</td><td>80 85 90</td><td/><td/><td/></tr><tr><td/><td>75</td><td/><td/><td/></tr><tr><td/><td/><td/><td>Birds</td><td>Cars</td><td>Aircrafts</td><td>Dogs</td><td>Flowers</td></tr><tr><td colspan="2">Greyscale Figure 2: Birds 40 50 60 70 80 90 Accuracy ProtoNet (5-way) Cars Low-res.</td><td>Aircrafts Low-res.</td><td colspan="2">Dogs Greyscale ProtoNet + Jigsaw (5-way) Flowers Greyscale</td><td>Birds 20% ProtoNet (20-way) Cars 20%</td><td>Aircrafts 20% ProtoNet + Jigsaw (20-way) Dogs 20% Flowers 20%</td></tr></table>

Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. Unsupervised feature learning via nonparametric instance discrimination. In Computer Vision and Pattern Recognition (CVPR), 2018. 2 Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In European Conference on Computer Vision (ECCV), 2016. 2The statistics of various datasets used in our experiments are shown in Table1. We show results on several fine-grained classification datasets in addition to standard few-shot benchmarks such as miniand tiered-ImageNet. Notably, the fine-grained datasets are significantly smaller. Example

Table content: <table><tr><td colspan="6">Richard Zhang, Phillip Isola, and Alexei A Efros. Split-brain autoencoders: Unsupervised learning</td></tr><tr><td colspan="6">by cross-channel prediction. In Proceedings of the IEEE Conference on Computer Vision and</td></tr><tr><td colspan="4">Pattern Recognition (CVPR), 2017. 2</td><td/></tr><tr><td colspan="2">A APPENDIX</td><td/><td/><td/></tr><tr><td colspan="3">A.1 DATASET DETAILS</td><td/><td/></tr><tr><td>Birds</td><td/><td>Cars</td><td/><td>Aircrafts</td><td>Dogs</td><td>Flowers</td></tr><tr><td/><td/><td/><td>mini-</td><td>tiered-</td></tr><tr><td>Setting</td><td>Set</td><td>Stats</td><td>ImageNet</td><td colspan="2">ImageNet Birds Cars Aircrafts Dogs Flowers</td></tr><tr><td/><td>Base</td><td colspan="2"># class # images 38,400 64</td><td colspan="2">351 448,695 5885 8162 5000 10337 4129 100 98 50 60 51</td></tr><tr><td>Few-shot transfer</td><td>Val</td><td colspan="2"># class # images 9,600 16</td><td colspan="2">97 124,261 2950 3993 2500 50 49 25</td><td>30 5128 2113 26</td></tr><tr><td/><td>Novel</td><td colspan="2"># class # images 12,000 20</td><td colspan="2">160 206,209 2953 4030 2500 50 49 25</td><td>30 5115 1947 25</td></tr></table>



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Peer review (iclr2020):
Summary: There are three main contributions of the paper: i) The authors present an empirical study of different self-supervised learning (SSL) methods in the context of self-supervised learning. ii) They point out how SSL helps more when the dataset is harder. iii) They point out how domain matters while using SSL for training and present a method to choose samples from an unlabeled dataset. Strengths: 1) They confirm the results of [7] and provide additional evidence of the benefit of the self-supervised learning in the few-shot setting. They also showcase an interesting new result that self-supervised learning helps more in case of harder problems. 2) The authors have a done a commendable job of coming up with a meaningful set of experiments by varying base-models, self-supervised methods, datasets, and few-shot learning methods in Section 4.1. This is quite a comprehensive study. 3) The paper is well-written and well-motivated. Weaknesses: 1) The main weakness of the paper is Section 4.2*s experimental setup. i) The definition of domain distance in not quite meaningful. Since the chosen datasets have very different classes (airplanes vs dogs etc), the average embeddings for different datasets/classes will be far from each other. In Figure 4d, it is misleading to show a trendline that includes the same domain as that will always be 0. If that datapoint is removed the trend line is mostly flat. The authors want to present a quantifiable way to show how domain distribution affect performance on self-supervised learning methods. But this definition of domain distance is more meaningful in the domain adaptation setting (like Amazon-Office dataset used for domain adaptation (https://people.eecs.berkeley.edu/~jhoffman/domainadapt/)) as in that case the requirement is to get the embeddings close to each other for the same class but from different domains. ii) The authors go on to create an *unlabeled pool* by combining images from many domains. Then they train a domain classifier by labeling in-domain images as positive and labeling the images in pool as negative. Considering all images in the pool as negative is not correct as there can be images of same class in unlabeled pool. iii) Then they choose the samples which the classifier predicts comes from the same domain. This probably succeeds as it is done on top of ResNet-101 features (that has seen all of ImageNet). This technique probably works possibly due to the ResNet being pre-trained with so many labeled classes. One baseline might have just been to choose the k-nearest neighbors (from unlabled pool) to the average embedding of all the images of the chosen dataset. Since, it is quite easy for classifiers to detect from which dataset an image came from [6] it might be easy to choose an image from similar domain by using nearest neighbor in the embedding space. I am not sure how well their heuristic will work for an unlabeled pool that the classifier has never seen. Additionally, a portion of the dataset has been created by combining existing datasets. Since statistics of different datasets vary a lot artificially, the creation of an unlabeled pool by combining different datasets might work in the favor of the proposed heuristic of looking at *domain distance* to choose the samples. 2) Effect of domain shift in SSL (Figure 4b) is studying an extreme case where the domain shift results in almost no common classes in the SSL training phase. It would make more sense to include datasets where at least some of the classes are shared so that self-supervised learning methods get to see some relevant classes. In practice, a self-supervised learning method would be applied on a large unlabeled pool of images. Hopefully with increasing diversity and number of images, there might be some images on which ding SSL helps the downstream few-shot task. Hence comparison with such a dataset like ImageNet/iNatrualist is important here. Decision: The paper presents an well thought-out empirical study on self-supervised learning for few-shot learning. But there are major concerns with the empirical setup and methods presented in the section where they propose a method to choose samples for SSL from an unlabeled pool of images. Minor Comments: 1) “In contrast, we humans can quickly learn new concepts from limited training data” - Remove we. 2) “Despite recent advances, these techniques have only been applied to a few domains (e.g., entry-level classes on internet imagery), and under the assumption that large amounts of unlabeled images are available.” This is not true. Self-supervised learning methods have been used for continuous control in reinforcement learning[1, 2], cross-modal learning[3], navigation [5], action recognition[4] etc. Later on in related work the authors list many papers that use self-supervised learning in different contexts. This line should be modified to reflect how common self-supervised learning methods are in other fields as well and with less data. 3) “making the rotation task too hard or too trivial to benefit main task” - add respectively. 4) *With random sampling, the extra unlabeled data often hurts the performance, while those sampled using the “domain weighs” improves performance on most datasets.* replace weighs with weights 5) [8] points out how self-supervised learning on a large dataset but with a domain shift (YFCC100M) is not as effective for pre-training as it is doing self-supervised learning on the downstream task*s dataset (ImageNet). While it is a different setting it is inline with one of the main conclusions of the paper. References: [1] “PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations” Rico Jonschkowski, Roland Hafner, Jonathan Scholz, and Martin Riedmiller. [2] “Learning Actionable Representations from Visual Observations” Debidatta Dwibedi, Jonathan Tompson, Corey Lynch, Pierre Sermanet [3] “Look, Listen and Learn” Relja Arandjelovi?, Andrew Zisserman [4] “Self-supervised Spatiotemporal Learning via Video Clip Order Prediction” Dejing Xu, Jun Xiao, Zhou Zhao, Jian Shao, Di Xie, Yueting Zhuang. [5] “Scaling and Benchmarking Self-Supervised Visual Representation Learning” Priya Goyal, Dhruv Mahajan, Abhinav Gupta, Ishan Misra [6] *Unbiased Look at Dataset Bias* Antonio Torralba and Alexei A. Efros. [7] *Boosting ´few-shot visual learning with self-supervision.* Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Perez, and Matthieu Cord. [8] *Deep clustering for unsupervised learning of visual features* Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze