Self-Supervised Models are Continual Learners

• URL: http://arxiv.org/abs/2112.04215v1
• Date: Wed, 8 Dec 2021 10:39:13 GMT
• Title: Self-Supervised Models are Continual Learners
• Authors: Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, Julien Mairal
• Abstract summary: We show that self-supervised loss functions can be seamlessly converted into distillation mechanisms for Continual Learning. We devise a framework for Continual self-supervised visual representation Learning that significantly improves the quality of the learned representations.
• Score: 79.70541692930108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale. However, their efficacy is catastrophically reduced in a Continual Learning (CL) scenario where data is presented to the model sequentially. In this paper, we show that self-supervised loss functions can be seamlessly converted into distillation mechanisms for CL by adding a predictor network that maps the current state of the representations to their past state. This enables us to devise a framework for Continual self-supervised visual representation Learning that (i) significantly improves the quality of the learned representations, (ii) is compatible with several state-of-the-art self-supervised objectives, and (iii) needs little to no hyperparameter tuning. We demonstrate the effectiveness of our approach empirically by training six popular self-supervised models in various CL settings.

Related papers

• What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights [67.72413262980272]
  Severe data imbalance naturally exists among web-scale vision-language datasets. We find CLIP pre-trained thereupon exhibits notable robustness to the data imbalance compared to supervised learning. The robustness and discriminability of CLIP improve with more descriptive language supervision, larger data scale, and broader open-world concepts.
  arXiv  Detail & Related papers  (2024-05-31T17:57:24Z)
• Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations [19.800907485589402]
  Fine-tuning pre-trained vision-language models, like CLIP, has yielded success on diverse downstream tasks. These tuned models tend to become highly specialized, limiting their practicality for real-world deployment. We propose a lightweight representation calibration method for fine-tuning CLIP.
  arXiv  Detail & Related papers  (2024-03-12T01:47:17Z)
• 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)
• Continual Learners are Incremental Model Generalizers [70.34479702177988]
  This paper extensively studies the impact of Continual Learning (CL) models as pre-trainers. We find that the transfer quality of the representation often increases gradually without noticeable degradation in fine-tuning performance. We propose a new fine-tuning scheme, GLobal Attention Discretization (GLAD), that preserves rich task-generic representation during solving downstream tasks.
  arXiv  Detail & Related papers  (2023-06-21T05:26:28Z)
• Retrieval-Enhanced Contrastive Vision-Text Models [61.783728119255365]
  We propose to equip vision-text models with the ability to refine their embedding with cross-modal retrieved information from a memory at inference time. Remarkably, we show that this can be done with a light-weight, single-layer, fusion transformer on top of a frozen CLIP. Our experiments validate that our retrieval-enhanced contrastive (RECO) training improves CLIP performance substantially on several challenging fine-grained tasks.
  arXiv  Detail & Related papers  (2023-06-12T15:52:02Z)
• Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales [53.55369862746357]
  Self-supervised multi-object trackers have tremendous potential as they enable learning from raw domain-specific data. However, their re-identification accuracy still falls short compared to their supervised counterparts. We propose a training objective that enables self-supervised learning of re-identification features from multiple sequential frames.
  arXiv  Detail & Related papers  (2023-04-25T20:47:29Z)
• One Objective for All Models -- Self-supervised Learning for Topic Models [11.67381769733002]
  We highlight a key advantage of self-supervised learning -- when applied to data generated by topic models, self-supervised learning can be oblivious to the specific model. In particular, we prove that commonly used self-supervised objectives based on reconstruction or contrastive samples can both recover useful posterior information for general topic models.
  arXiv  Detail & Related papers  (2022-02-02T06:20:59Z)
• Revisiting Weakly Supervised Pre-Training of Visual Perception Models [27.95816470075203]
  Large-scale weakly supervised pre-training can outperform fully supervised approaches. This paper revisits weakly-supervised pre-training of models using hashtag supervision. Our results provide a compelling argument for the use of weakly supervised learning in the development of visual recognition systems.
  arXiv  Detail & Related papers  (2022-01-20T18:55:06Z)
• Robust Disentanglement of a Few Factors at a Time [5.156484100374058]
  We introduce population-based training (PBT) for improving consistency in training variational autoencoders (VAEs) We then use Unsupervised Disentanglement Ranking (UDR) as an unsupervised to score models in our PBT-VAE training and show how models trained this way tend to consistently disentangle only a subset of the generative factors. We show striking improvement in state-of-the-art unsupervised disentanglement performance and robustness across multiple datasets and metrics.
  arXiv  Detail & Related papers  (2020-10-26T12:34:23Z)

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.