Affine transformation estimation improves visual self-supervised learning

• URL: http://arxiv.org/abs/2402.09071v1
• Date: Wed, 14 Feb 2024 10:32:58 GMT
• Title: Affine transformation estimation improves visual self-supervised learning
• Authors: David Torpey and Richard Klein
• Abstract summary: We show that adding a module to constrain the representations to be predictive of an affine transformation improves the performance and efficiency of the learning process. We perform experiments in various modern self-supervised models and see a performance improvement in all cases.
• Score: 4.40560654491339
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The standard approach to modern self-supervised learning is to generate random views through data augmentations and minimise a loss computed from the representations of these views. This inherently encourages invariance to the transformations that comprise the data augmentation function. In this work, we show that adding a module to constrain the representations to be predictive of an affine transformation improves the performance and efficiency of the learning process. The module is agnostic to the base self-supervised model and manifests in the form of an additional loss term that encourages an aggregation of the encoder representations to be predictive of an affine transformation applied to the input images. We perform experiments in various modern self-supervised models and see a performance improvement in all cases. Further, we perform an ablation study on the components of the affine transformation to understand which of them is affecting performance the most, as well as on key architectural design decisions.

Related papers

• Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look [28.350278251132078]
  We propose a unified framework to conduct data augmentation in the feature space, known as feature augmentation. This strategy is domain-agnostic, which augments similar features to the original ones and thus improves the data diversity.
  arXiv  Detail & Related papers  (2024-10-16T09:25:11Z)
• Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance [61.06245197347139]
  We propose a novel approach to explain the behavior of a black-box model under feature shifts. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation.
  arXiv  Detail & Related papers  (2024-08-24T18:28:19Z)
• DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning [75.68193159293425]
  In-context learning (ICL) allows transformer-based language models to learn a specific task with a few "task demonstrations" without updating their parameters. We propose an influence function-based attribution technique, DETAIL, that addresses the specific characteristics of ICL. We experimentally prove the wide applicability of DETAIL by showing our attribution scores obtained on white-box models are transferable to black-box models in improving model performance.
  arXiv  Detail & Related papers  (2024-05-22T15:52:52Z)
• Self-Supervised Representation Learning with Meta Comprehensive Regularization [11.387994024747842]
  We introduce a module called CompMod with Meta Comprehensive Regularization (MCR), embedded into existing self-supervised frameworks. We update our proposed model through a bi-level optimization mechanism, enabling it to capture comprehensive features. We provide theoretical support for our proposed method from information theory and causal counterfactual perspective.
  arXiv  Detail & Related papers  (2024-03-03T15:53:48Z)
• Uncovering mesa-optimization algorithms in Transformers [61.06055590704677]
  Some autoregressive models can learn as an input sequence is processed, without undergoing any parameter changes, and without being explicitly trained to do so. We show that standard next-token prediction error minimization gives rise to a subsidiary learning algorithm that adjusts the model as new inputs are revealed. Our findings explain in-context learning as a product of autoregressive loss minimization and inform the design of new optimization-based Transformer layers.
  arXiv  Detail & Related papers  (2023-09-11T22:42:50Z)
• ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers [7.725095281624494]
  We evaluate the effectiveness of Masked Autoencoding as a pretraining scheme, and explore Momentum Contrast as an alternative. We observe that the transformer learns to attend to semantically meaningful regions, indicating that pretraining leads to a better understanding of the underlying geometry.
  arXiv  Detail & Related papers  (2023-06-19T09:38:21Z)
• Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective [72.55093886515824]
  We introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables. We devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph. Experiment results on synthetic and real datasets show that our three proposed components significantly improve the robustness and reusability of the learned motion representations.
  arXiv  Detail & Related papers  (2021-11-29T18:59:09Z)
• Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles [60.97922557957857]
  We provide a framework to perform self-supervised model ensembling via a novel method of learning representations directly through gradient descent at inference time. This technique improves representation quality, as measured by k-nearest neighbors, both on the in-domain dataset and in the transfer setting.
  arXiv  Detail & Related papers  (2021-10-19T22:24:57Z)
• More Is More -- Narrowing the Generalization Gap by Adding Classification Heads [8.883733362171032]
  We introduce an architecture enhancement for existing neural network models based on input transformations, termed 'TransNet' Our model can be employed during training time only and then pruned for prediction, resulting in an equivalent architecture to the base model.
  arXiv  Detail & Related papers  (2021-02-09T16:30:33Z)
• Explicit homography estimation improves contrastive self-supervised learning [0.30458514384586394]
  We propose a module that serves as an additional objective in the self-supervised contrastive learning paradigm. We show how the inclusion of this module to regress the parameters of an affine transformation or homography improves both performance and learning speed.
  arXiv  Detail & Related papers  (2021-01-12T19:33:37Z)
• Guided Variational Autoencoder for Disentanglement Learning [79.02010588207416]
  We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. We design an unsupervised strategy and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE.
  arXiv  Detail & Related papers  (2020-04-02T20:49:15Z)

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.