Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features

• URL: http://arxiv.org/abs/2407.17486v1
• Date: Wed, 3 Jul 2024 06:46:08 GMT
• Title: Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features
• Authors: Thalles Silva, Helio Pedrini, Adín Ramírez Rivera,
• Abstract summary: The proposed method involves augmenting a neural network with a memory component to compare current image views with previously encountered concepts. We benchmark our method on many vision tasks, such as linear, transfer learning, low-shot classification, and image retrieval on many datasets. The experimental results consolidate the effectiveness of the proposed approach in achieving stable SSL training without additional regularizers.
• Score: 6.096888891865663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel approach to improving the training stability of self-supervised learning (SSL) methods by leveraging a non-parametric memory of seen concepts. The proposed method involves augmenting a neural network with a memory component to stochastically compare current image views with previously encountered concepts. Additionally, we introduce stochastic memory blocks to regularize training and enforce consistency between image views. We extensively benchmark our method on many vision tasks, such as linear probing, transfer learning, low-shot classification, and image retrieval on many datasets. The experimental results consolidate the effectiveness of the proposed approach in achieving stable SSL training without additional regularizers while learning highly transferable representations and requiring less computing time and resources.

Related papers

• Gradient-Free Supervised Learning using Spike-Timing-Dependent Plasticity for Image Recognition [3.087000217989688]
  An approach to supervised learning in spiking neural networks is presented using a gradient-free method combined with spike-timing-dependent plasticity for image recognition. The proposed network architecture is scalable to multiple layers, enabling the development of more complex and deeper SNN models.
  arXiv  Detail & Related papers  (2024-10-21T21:32:17Z)
• Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning [12.5354658533836]
  Humans possess remarkable ability to accurately classify new, unseen images after being exposed to only a few examples. For artificial neural network models, determining the most relevant features for distinguishing between two images with limited samples presents a challenge. We propose an intra-task mutual attention method for few-shot learning, that involves splitting the support and query samples into patches.
  arXiv  Detail & Related papers  (2024-05-06T02:02:57Z)
• Data-efficient Event Camera Pre-training via Disentangled Masked Modeling [20.987277885575963]
  We present a new data-supervised voxel-based self-supervised learning method for event cameras. Our method overcomes the limitations of previous methods, which either sacrifice temporal information or directly employ paired image data. It exhibits excellent generalization performance and demonstrates significant improvements across various tasks with fewer parameters and lower computational costs.
  arXiv  Detail & Related papers  (2024-03-01T10:02:25Z)
• Improving Image Recognition by Retrieving from Web-Scale Image-Text Data [68.63453336523318]
  We introduce an attention-based memory module, which learns the importance of each retrieved example from the memory. Compared to existing approaches, our method removes the influence of the irrelevant retrieved examples, and retains those that are beneficial to the input query. We show that it achieves state-of-the-art accuracies in ImageNet-LT, Places-LT and Webvision datasets.
  arXiv  Detail & Related papers  (2023-04-11T12:12:05Z)
• A Memory Transformer Network for Incremental Learning [64.0410375349852]
  We study class-incremental learning, a training setup in which new classes of data are observed over time for the model to learn from. Despite the straightforward problem formulation, the naive application of classification models to class-incremental learning results in the "catastrophic forgetting" of previously seen classes. One of the most successful existing methods has been the use of a memory of exemplars, which overcomes the issue of catastrophic forgetting by saving a subset of past data into a memory bank and utilizing it to prevent forgetting when training future tasks.
  arXiv  Detail & Related papers  (2022-10-10T08:27:28Z)
• Robust Cross-Modal Representation Learning with Progressive Self-Distillation [7.676408770854477]
  The learning objective of vision-language approach of CLIP does not effectively account for the noisy many-to-many correspondences found in web-harvested image captioning datasets. We introduce a novel training framework based on cross-modal contrastive learning that uses progressive self-distillation and soft image-text alignments to more efficiently learn robust representations from noisy data.
  arXiv  Detail & Related papers  (2022-04-10T03:28:18Z)
• 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)
• ReSSL: Relational Self-Supervised Learning with Weak Augmentation [68.47096022526927]
  Self-supervised learning has achieved great success in learning visual representations without data annotations. We introduce a novel relational SSL paradigm that learns representations by modeling the relationship between different instances. Our proposed ReSSL significantly outperforms the previous state-of-the-art algorithms in terms of both performance and training efficiency.
  arXiv  Detail & Related papers  (2021-07-20T06:53:07Z)
• Unsupervised Learning of Visual Features by Contrasting Cluster Assignments [57.33699905852397]
  We propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Our method simultaneously clusters the data while enforcing consistency between cluster assignments. Our method can be trained with large and small batches and can scale to unlimited amounts of data.
  arXiv  Detail & Related papers  (2020-06-17T14:00:42Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)

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.