Self-Supervised Learning of Pretext-Invariant Representations (1912.01991v1)

Abstract: The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations for a large training set of images. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as "pearl") that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new state-of-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised learning of image representations with good invariance properties.

• The paper introduces PIRL, a self-supervised framework that learns invariant representations by minimizing feature differences between original and transformed images.
• The paper employs a contrastive loss function with a Jigsaw Puzzle pretext task to encourage semantic invariance across diverse image transformations.
• The paper demonstrates significant improvements on benchmarks like ImageNet and Pascal VOC, outperforming several state-of-the-art self-supervised methods.

An Insightful Overview of "Self-Supervised Learning of Pretext-Invariant Representations"

The paper "Self-Supervised Learning of Pretext-Invariant Representations" by Misra and van der Maaten introduces a novel method called Pretext-Invariant Representation Learning (PIRL), aimed at improving the effectiveness of self-supervised learning for image representation. Unlike existing pretext tasks that encourage representations covariant to image transformations, PIRL focuses on learning invariant representations that maintain semantic integrity irrespective of such transformations.

Background and Motivation

Current image recognition systems depend heavily on large-scale annotated datasets to train models capable of understanding visual content. This reliance on semantic annotations poses significant scalability issues, particularly for the long tail of visual concepts. Self-supervised learning addresses these limitations by using the raw data itself to generate supervisory signals, typically through pretext tasks. However, many pretext tasks lead to representations covariant with image transformations, which adversely affect their utility in semantic recognition tasks.

Methodology

PIRL redefines the learning objective for self-supervised models by promoting invariant representations. Instead of predicting properties of image transformations, PIRL ensures that the representations of original and transformed versions of an image are close to each other in the feature space. This is achieved using a contrastive loss function implemented via a noise contrastive estimator (NCE).

The PIRL framework was applied using the Jigsaw Puzzle pretext task, a popular approach in self-supervised learning. The image is divided into nine patches, randomly permuted, and the model learns to generate consistent representations invariant to this transformation. The empirical risk minimization approach in PIRL encourages the network to produce similar features for an image and its transformed counterpart, thus achieving the desired invariance.

Experimental Results

The paper evaluates PIRL on multiple benchmarks, including ImageNet, Pascal VOC, Places205, and iNaturalist. The results demonstrate significant improvements across a range of image classification and object detection tasks. Notably:

• Image Classification: PIRL achieves top-tier performance with single-crop top-1 accuracy on ImageNet, outperforming existing self-supervised methods.
• Object Detection: In transfer learning to object detection tasks, PIRL sets a new state-of-the-art, even surpassing some supervised pretraining baselines. Specifically, PIRL achieves superior performance on the VOC07 and VOC07+12 train splits using the Faster R-CNN architecture with ResNet-50 backbone.
• Semi-Supervised Learning: PIRL exhibits robustness when finetuned on limited labeled data from ImageNet, achieving commendable top-5 accuracy in semi-supervised settings.

Analysis and Discussions

Through extensive analysis, the paper highlights several insights:

• Invariance Properties: PIRL effectively learns representations that are invariant to the applied transformations. Distance metrics between representations of original and transformed images validate this property.
• Layer-wise Performance: PIRL representations extracted from the res5 layer of the network demonstrate superior performance. In contrast, covariant representations tend to degrade in deeper layers.
• Trade-off Parameters: The hyperparameter $\lambda$, which balances the NCE losses, significantly influences representation quality. Optimal results are obtained when $\lambda$ is set to 0.5.
• Generalizability: While the paper focuses on Jigsaw Puzzles, PIRL also shows potential with other pretext tasks like image rotations.

Implications and Future Directions

PIRL brings substantial advancements to self-supervised learning paradigms by emphasizing invariance in learned representations. This approach not only enhances semantic understanding but also integrates smoothly with various pretext tasks. The implications of this research are multifaceted:

• Practical Applications: Enhanced self-supervised representations can effectively reduce dependency on large annotated datasets, thus facilitating high-quality models for diverse and less-studied visual domains.
• Theoretical Developments: The concept of pretext-invariance opens new avenues for self-supervised learning research, potentially merging with clustering-based methods and complex transformation sets for more robust representations.

The paper signifies a crucial step towards more generalized and semantically rich image representations, with promising extensions in combining multiple pretext tasks and exploring richer transformation sets. As self-supervised learning continues to evolve, PIRL stands out as a compelling direction for advancing the field.