Tailoring Self-Supervision for Supervised Learning (2207.10023v1)

Abstract: Recently, it is shown that deploying a proper self-supervision is a prospective way to enhance the performance of supervised learning. Yet, the benefits of self-supervision are not fully exploited as previous pretext tasks are specialized for unsupervised representation learning. To this end, we begin by presenting three desirable properties for such auxiliary tasks to assist the supervised objective. First, the tasks need to guide the model to learn rich features. Second, the transformations involved in the self-supervision should not significantly alter the training distribution. Third, the tasks are preferred to be light and generic for high applicability to prior arts. Subsequently, to show how existing pretext tasks can fulfill these and be tailored for supervised learning, we propose a simple auxiliary self-supervision task, predicting localizable rotation (LoRot). Our exhaustive experiments validate the merits of LoRot as a pretext task tailored for supervised learning in terms of robustness and generalization capability. Our code is available at https://github.com/wjun0830/Localizable-Rotation.

• The paper introduces LoRot, a novel auxiliary task that tailors self-supervision to improve supervised learning performance.
• Experiments show enhanced out-of-distribution detection with high AUROC scores and significant gains in ImageNet classification.
• The approach boosts adversarial robustness and transfer learning by preserving image context and refining feature extraction.

Tailoring Self-Supervision for Supervised Learning

The paper by Moon et al., "Tailoring Self-Supervision for Supervised Learning," presents an innovative approach to enhancing supervised learning through self-supervision. This paper critically examines the limitations of existing self-supervised methods typically designed for unsupervised representation learning, which often fail to efficiently integrate with supervised objectives due to significant data distribution shifts and specialized pretext tasks. The authors propose a novel auxiliary self-supervision task, termed Localizable Rotation (LoRot), specifically tailored to support supervised learning, aiming to demonstrate how self-supervision can be more effectively implemented in supervised contexts.

Key Contributions and Methodology

The paper begins with an exploration of the general properties that make self-supervised tasks beneficial for supervised learning. Key attributes identified include the ability of tasks to guide models to learn rich features, minimize shifting of the data distribution, and maintain high applicability to existing models and architectures with minimal computational overhead.

Following this framework, the authors introduce LoRot, which focuses on localizing rotation within images. LoRot involves rotating a localized part of an image to create a self-supervised learning task. Unlike global rotation tasks, which can significantly alter the underlying data distribution and potentially degrade primary task performance, LoRot maintains more of the image's original context while challenging the model to learn more detailed and localized features.

LoRot is evaluated through extensive experiments against both traditional rotation-based tasks and state-of-the-art methods. The paper reports robust performance and notable enhancements in generalization capabilities across various benchmarks, such as image classification, out-of-distribution detection, imbalanced classification, adversarial attack resilience, and transfer learning tasks.

Experimental Results and Implications

1. Out-of-Distribution (OOD) Detection: In experiments involving OOD detection tasks using CIFAR-10 and different OOD datasets such as SVHN and LSUN, LoRot demonstrated superior detection capabilities. Notably, it achieved high AUROC scores, signifying its effectiveness in enhancing robustness against unknown data.
2. Classification Accuracy: On large-scale datasets like ImageNet, LoRot yielded competitive results, significantly enhancing the robustness and generalization of ResNet-based architectures. It also showed complementary benefits when used alongside popular augmentation strategies like Mixup and AutoAugment.
3. Imbalanced Classification and Adversarial Robustness: LoRot showcased considerable improvements over the baseline in handling class imbalances and defending against adversarial perturbations, suggesting its potential to contribute positively to real-world robust learning models.
4. Transfer Learning and Localization: The ability of LoRot-pretrained models to excel in localization-oriented tasks such as weakly supervised object localization and transfer learning for object detection highlights its utility in extracting and understanding spatial hierarchies within visual data.

Theoretical and Practical Implications

The introduction of LoRot represents a meaningful shift towards customizing self-supervision for supervised learning contexts. The findings imply that self-supervised auxiliary tasks should not merely transplant concepts from their unsupervised counterparts but instead be redesigned to complement the objectives and constraints of supervised learning environments. By focusing on local transformations, LoRot circumvents many pitfalls of global data alterations.

Practically, this work paves the way for integrating more efficient, computationally light self-supervision techniques into existing ML workflows, potentially reducing the computational overhead while maintaining or improving model performance.

Future Directions

The results presented in this paper suggest several avenues for further research. Examining the impact of applying similar localized transformations to other types of self-supervised tasks could be beneficial. Additionally, investigating more complex transformations or combining multiple transformations to further decouple self-supervised and supervised objectives could yield further gains in specialized domains.

Moon et al.'s approach highlights the synergy between innovative task formulation and model learning, potentially inspiring future innovations in the field of augmentation and self-supervised learning techniques that align closely with specific supervised tasks.

This work provides a compelling case for revisiting and refining self-supervised strategies, demonstrating that when designed with appropriate constraints and objectives, self-supervision can substantially elevate the performance of supervised models.