Deciphering the Projection Head: Representation Evaluation Self-supervised Learning

• URL: http://arxiv.org/abs/2301.12189v1
• Date: Sat, 28 Jan 2023 13:13:53 GMT
• Title: Deciphering the Projection Head: Representation Evaluation Self-supervised Learning
• Authors: Jiajun Ma, Tianyang Hu, Wenjia Wang
• Abstract summary: Self-supervised learning (SSL) aims to learn intrinsic features without labels. Projection head always plays an important role in improving the performance of the downstream task. We propose a Representation Evaluation Design (RED) in SSL models in which a shortcut connection between the representation and the projection vectors is built.
• Score: 6.375931203397043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) aims to learn intrinsic features without labels. Despite the diverse architectures of SSL methods, the projection head always plays an important role in improving the performance of the downstream task. In this work, we systematically investigate the role of the projection head in SSL. Specifically, the projection head targets the uniformity part of SSL, which pushes the dissimilar samples away from each other, thus enabling the encoder to focus on extracting semantic features. Based on this understanding, we propose a Representation Evaluation Design (RED) in SSL models in which a shortcut connection between the representation and the projection vectors is built. Extensive experiments with different architectures, including SimCLR, MoCo-V2, and SimSiam, on various datasets, demonstrate that the representation evaluation design can consistently improve the baseline models in the downstream tasks. The learned representation from the RED-SSL models shows superior robustness to unseen augmentations and out-of-distribution data.

Related papers

• A Probabilistic Model Behind Self-Supervised Learning [53.64989127914936]
  In self-supervised learning (SSL), representations are learned via an auxiliary task without annotated labels. We present a generative latent variable model for self-supervised learning. We show that several families of discriminative SSL, including contrastive methods, induce a comparable distribution over representations.
  arXiv  Detail & Related papers  (2024-02-02T13:31:17Z)
• Enhancing Representations through Heterogeneous Self-Supervised Learning [61.40674648939691]
  We propose Heterogeneous Self-Supervised Learning (HSSL), which enforces a base model to learn from an auxiliary head whose architecture is heterogeneous from the base model. The HSSL endows the base model with new characteristics in a representation learning way without structural changes. The HSSL is compatible with various self-supervised methods, achieving superior performances on various downstream tasks.
  arXiv  Detail & Related papers  (2023-10-08T10:44:05Z)
• Hierarchical Visual Primitive Experts for Compositional Zero-Shot Learning [52.506434446439776]
  Compositional zero-shot learning (CZSL) aims to recognize compositions with prior knowledge of known primitives (attribute and object) We propose a simple and scalable framework called Composition Transformer (CoT) to address these issues. Our method achieves SoTA performance on several benchmarks, including MIT-States, C-GQA, and VAW-CZSL.
  arXiv  Detail & Related papers  (2023-08-08T03:24:21Z)
• Augmentation-aware Self-supervised Learning with Conditioned Projector [6.720605329045581]
  Self-supervised learning (SSL) is a powerful technique for learning from unlabeled data. We propose to foster sensitivity to characteristics in the representation space by modifying the projector network. Our approach, coined Conditional Augmentation-aware Self-supervised Learning (CASSLE), is directly applicable to typical joint-embedding SSL methods.
  arXiv  Detail & Related papers  (2023-05-31T12:24:06Z)
• Reverse Engineering Self-Supervised Learning [17.720366509919167]
  Self-supervised learning (SSL) is a powerful tool in machine learning. This paper presents an in-depth empirical analysis of SSL-trained representations.
  arXiv  Detail & Related papers  (2023-05-24T23:15:28Z)
• Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition [2.2082422928825136]
  Self-supervised learning (SSL) frameworks have been extensively applied to sensor-based Human Activity Recognition (HAR) In this paper, we aim to analyze deep representations of two recent SSL frameworks, namely SimCLR and VICReg.
  arXiv  Detail & Related papers  (2023-04-14T07:53:59Z)
• Understanding and Improving the Role of Projection Head in Self-Supervised Learning [77.59320917894043]
  Self-supervised learning (SSL) aims to produce useful feature representations without access to human-labeled data annotations. Current contrastive learning approaches append a parametrized projection head to the end of some backbone network to optimize the InfoNCE objective. This raises a fundamental question: Why is a learnable projection head required if we are to discard it after training?
  arXiv  Detail & Related papers  (2022-12-22T05:42:54Z)
• Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods [61.49061000562676]
  We introduce Cluster Learnability (CL) to assess learnability. CL is measured in terms of the performance of a KNN trained to predict labels obtained by clustering the representations with K-means. We find that CL better correlates with in-distribution model performance than other competing recent evaluation schemes.
  arXiv  Detail & Related papers  (2022-06-02T19:05:13Z)
• Weak Augmentation Guided Relational Self-Supervised Learning [80.0680103295137]
  We introduce a novel relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Our proposed method employs sharpened distribution of pairwise similarities among different instances as textitrelation metric. Experimental results show that our proposed ReSSL substantially outperforms the state-of-the-art methods across different network architectures.
  arXiv  Detail & Related papers  (2022-03-16T16:14:19Z)
• High Fidelity Visualization of What Your Self-Supervised Representation Knows About [22.982471878833362]
  In this work, we showcase the use of a conditional diffusion based generative model (RCDM) to visualize representations learned with self-supervised models. We demonstrate how this model's generation quality is on par with state-of-the-art generative models while being faithful to the representation used as conditioning.
  arXiv  Detail & Related papers  (2021-12-16T19:23:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.