A Practitioner's Guide to Continual Multimodal Pretraining

• URL: http://arxiv.org/abs/2408.14471v1
• Date: Mon, 26 Aug 2024 17:59:01 GMT
• Title: A Practitioner's Guide to Continual Multimodal Pretraining
• Authors: Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio, Ameya Prabhu, Mehdi Cherti, Oriol Vinyals, Olivier Hénaff, Samuel Albanie, Matthias Bethge, Zeynep Akata,
• Abstract summary: Multimodal foundation models serve numerous applications at the intersection of vision and language. To keep models updated, research into continual pretraining mainly explores scenarios with either infrequent, indiscriminate updates on large-scale new data, or frequent, sample-level updates. We introduce FoMo-in-Flux, a continual multimodal pretraining benchmark with realistic compute constraints and practical deployment requirements.
• Score: 83.63894495064855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal foundation models serve numerous applications at the intersection of vision and language. Still, despite being pretrained on extensive data, they become outdated over time. To keep models updated, research into continual pretraining mainly explores scenarios with either (1) infrequent, indiscriminate updates on large-scale new data, or (2) frequent, sample-level updates. However, practical model deployment often operates in the gap between these two limit cases, as real-world applications often demand adaptation to specific subdomains, tasks or concepts -- spread over the entire, varying life cycle of a model. In this work, we complement current perspectives on continual pretraining through a research test bed as well as provide comprehensive guidance for effective continual model updates in such scenarios. We first introduce FoMo-in-Flux, a continual multimodal pretraining benchmark with realistic compute constraints and practical deployment requirements, constructed over 63 datasets with diverse visual and semantic coverage. Using FoMo-in-Flux, we explore the complex landscape of practical continual pretraining through multiple perspectives: (1) A data-centric investigation of data mixtures and stream orderings that emulate real-world deployment situations, (2) a method-centric investigation ranging from simple fine-tuning and traditional continual learning strategies to parameter-efficient updates and model merging, (3) meta learning rate schedules and mechanistic design choices, and (4) the influence of model and compute scaling. Together, our insights provide a practitioner's guide to continual multimodal pretraining for real-world deployment. Our benchmark and code is here: https://github.com/ExplainableML/fomo_in_flux.

Related papers

• Specialized Foundation Models Struggle to Beat Supervised Baselines [60.23386520331143]
  We look at three modalities -- genomics, satellite imaging, and time series -- with multiple recent FMs and compare them to a standard supervised learning workflow. We find that it is consistently possible to train simple supervised models that match or even outperform the latest foundation models.
  arXiv  Detail & Related papers  (2024-11-05T04:10:59Z)
• Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework [58.362064122489166]
  This paper introduces the Cross-modal Few-Shot Learning task, which aims to recognize instances from multiple modalities when only a few labeled examples are available. We propose a Generative Transfer Learning framework consisting of two stages: the first involves training on abundant unimodal data, and the second focuses on transfer learning to adapt to novel data. Our finds demonstrate that GTL has superior performance compared to state-of-the-art methods across four distinct multi-modal datasets.
  arXiv  Detail & Related papers  (2024-10-14T16:09:38Z)
• ATLAS: Adapter-Based Multi-Modal Continual Learning with a Two-Stage Learning Strategy [12.150065431702055]
  We propose a multi-modal continual learning scheme that consists of experience-based learning and novel knowledge expansion. Our method is proficient for continual learning. It expands the distribution of representation upstream while also minimizing the negative impact of forgetting previous tasks.
  arXiv  Detail & Related papers  (2024-10-14T13:29:42Z)
• TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Prediction [7.3292387742640415]
  We propose to incorporate richer training dynamics information into a prototypical contrastive learning framework. We conduct empirical evaluations of our approach using two large-scale naturalistic datasets.
  arXiv  Detail & Related papers  (2024-04-18T23:12:46Z)
• Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning [54.67880602409801]
  In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of visual control tasks. We introduce Contextualized World Models (ContextWM) that explicitly separate context and dynamics modeling. Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample efficiency of model-based reinforcement learning.
  arXiv  Detail & Related papers  (2023-05-29T14:29:12Z)
• Predictive Experience Replay for Continual Visual Control and Forecasting [62.06183102362871]
  We present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting. We first propose the mixture world model that learns task-specific dynamics priors with a mixture of Gaussians, and then introduce a new training strategy to overcome catastrophic forgetting. Our model remarkably outperforms the naive combinations of existing continual learning and visual RL algorithms on DeepMind Control and Meta-World benchmarks with continual visual control tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:08:03Z)
• Learning Behavior Representations Through Multi-Timescale Bootstrapping [8.543808476554695]
  We introduce Bootstrap Across Multiple Scales (BAMS), a multi-scale representation learning model for behavior. We first apply our method on a dataset of quadrupeds navigating in different terrain types, and show that our model captures the temporal complexity of behavior.
  arXiv  Detail & Related papers  (2022-06-14T17:57:55Z)
• Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data [10.006890915441987]
  Popularity of self-supervised learning is driven by the fact that traditional models typically require a huge amount of well-annotated data for training. Self-supervised methods have been introduced to improve the efficiency of training data through discriminative pre-training of models. We aim to provide the first comprehensive review of multimodal self-supervised learning methods for temporal data.
  arXiv  Detail & Related papers  (2022-06-06T04:59:44Z)
• Conditional Generative Modeling via Learning the Latent Space [54.620761775441046]
  We propose a novel framework for conditional generation in multimodal spaces. It uses latent variables to model generalizable learning patterns. At inference, the latent variables are optimized to find optimal solutions corresponding to multiple output modes.
  arXiv  Detail & Related papers  (2020-10-07T03:11:34Z)

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.