Survey on Self-supervised Representation Learning Using Image Transformations

• URL: http://arxiv.org/abs/2202.08514v1
• Date: Thu, 17 Feb 2022 08:37:50 GMT
• Title: Survey on Self-supervised Representation Learning Using Image Transformations
• Authors: Muhammad Ali, Sayed Hashim
• Abstract summary: Self-supervised learning (SSL) is a technique used in unsupervised representation learning. geometric transformations have shown to be powerful supervisory signals in SSL. We shortlist six representative models that use image transformations including those based on predicting and autoencoding transformations. Our analysis indicates the AETv2 performs the best in most settings.
• Score: 0.8098097078441623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural networks need huge amount of training data, while in real world there is a scarcity of data available for training purposes. To resolve these issues, self-supervised learning (SSL) methods are used. SSL using geometric transformations (GT) is a simple yet powerful technique used in unsupervised representation learning. Although multiple survey papers have reviewed SSL techniques, there is none that only focuses on those that use geometric transformations. Furthermore, such methods have not been covered in depth in papers where they are reviewed. Our motivation to present this work is that geometric transformations have shown to be powerful supervisory signals in unsupervised representation learning. Moreover, many such works have found tremendous success, but have not gained much attention. We present a concise survey of SSL approaches that use geometric transformations. We shortlist six representative models that use image transformations including those based on predicting and autoencoding transformations. We review their architecture as well as learning methodologies. We also compare the performance of these models in the object recognition task on CIFAR-10 and ImageNet datasets. Our analysis indicates the AETv2 performs the best in most settings. Rotation with feature decoupling also performed well in some settings. We then derive insights from the observed results. Finally, we conclude with a summary of the results and insights as well as highlighting open problems to be addressed and indicating various future directions.

Related papers

• What Makes Pre-Trained Visual Representations Successful for Robust Manipulation? [57.92924256181857]
  We find that visual representations designed for manipulation and control tasks do not necessarily generalize under subtle changes in lighting and scene texture. We find that emergent segmentation ability is a strong predictor of out-of-distribution generalization among ViT models.
  arXiv  Detail & Related papers  (2023-11-03T18:09:08Z)
• MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments [72.6405488990753]
  Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks. We propose a single-stage and standalone method, MOCA, which unifies both desired properties. We achieve new state-of-the-art results on low-shot settings and strong experimental results in various evaluation protocols.
  arXiv  Detail & Related papers  (2023-07-18T15:46:20Z)
• Substance or Style: What Does Your Image Embedding Know? [55.676463077772866]
  Image foundation models have primarily been evaluated for semantic content. We measure the visual content of embeddings along many axes, including image style, quality, and a range of natural and artificial transformations. We find that image-text models (CLIP and ALIGN) are better at recognizing new examples of style transfer than masking-based models (CAN and MAE)
  arXiv  Detail & Related papers  (2023-07-10T22:40:10Z)
• Prediction of Geometric Transformation on Cardiac MRI via Convolutional Neural Network [13.01021780124613]
  We propose to learn features in medical images by training ConvNets to recognize the geometric transformation applied to images. We present a simple self-supervised task that can easily predict the geometric transformation.
  arXiv  Detail & Related papers  (2022-11-12T11:29:14Z)
• TransformNet: Self-supervised representation learning through predicting geometric transformations [0.8098097078441623]
  We describe the unsupervised semantic feature learning approach for recognition of the geometric transformation applied to the input data. The basic concept of our approach is that if someone is unaware of the objects in the images, he/she would not be able to quantitatively predict the geometric transformation that was applied to them.
  arXiv  Detail & Related papers  (2022-02-08T22:41:01Z)
• 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)
• Jointly Learnable Data Augmentations for Self-Supervised GNNs [0.311537581064266]
  We propose GraphSurgeon, a novel self-supervised learning method for graph representation learning. We take advantage of the flexibility of the learnable data augmentation and introduce a new strategy that augments in the embedding space. Our finding shows that GraphSurgeon is comparable to six SOTA semi-supervised and on par with five SOTA self-supervised baselines in node classification tasks.
  arXiv  Detail & Related papers  (2021-08-23T21:33:12Z)
• Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations [78.12377360145078]
  Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection. In this paper, we first study how biases in the dataset affect existing methods. We show that current contrastive approaches work surprisingly well across: (i) object- versus scene-centric, (ii) uniform versus long-tailed and (iii) general versus domain-specific datasets.
  arXiv  Detail & Related papers  (2021-06-10T17:59:13Z)
• Category-Learning with Context-Augmented Autoencoder [63.05016513788047]
  Finding an interpretable non-redundant representation of real-world data is one of the key problems in Machine Learning. We propose a novel method of using data augmentations when training autoencoders. We train a Variational Autoencoder in such a way, that it makes transformation outcome predictable by auxiliary network.
  arXiv  Detail & Related papers  (2020-10-10T14:04:44Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)

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.