Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts

• URL: http://arxiv.org/abs/2204.05248v1
• Date: Mon, 11 Apr 2022 16:46:50 GMT
• Title: Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts
• Authors: Quan Feng, Qingyuan Wu, Jiayu Yao, Songcan Chen
• Abstract summary: We propose a novel solution by leveraging the attention mechanism to adaptively squeeze suitable representations for the tasks. Our scheme significantly exceeds current popular pretext-matching based methods in gathering knowledge.
• Score: 20.764378638979704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL), as a newly emerging unsupervised representation learning paradigm, generally follows a two-stage learning pipeline: 1) learning invariant and discriminative representations with auto-annotation pretext(s), then 2) transferring the representations to assist downstream task(s). Such two stages are usually implemented separately, making the learned representation learned agnostic to the downstream tasks. Currently, most works are devoted to exploring the first stage. Whereas, it is less studied on how to learn downstream tasks with limited labeled data using the already learned representations. Especially, it is crucial and challenging to selectively utilize the complementary representations from diverse pretexts for a downstream task. In this paper, we technically propose a novel solution by leveraging the attention mechanism to adaptively squeeze suitable representations for the tasks. Meanwhile, resorting to information theory, we theoretically prove that gathering representation from diverse pretexts is more effective than a single one. Extensive experiments validate that our scheme significantly exceeds current popular pretext-matching based methods in gathering knowledge and relieving negative transfer in downstream tasks.

Related papers

• Conditional Prototype Rectification Prompt Learning [32.533844163120875]
  We propose a Prototype Rectification Prompt Learning (CPR) method to correct the bias of base examples and augment limited data in an effective way. CPR achieves state-of-the-art performance on both few-shot classification and base-to-new generalization tasks.
  arXiv  Detail & Related papers  (2024-04-15T15:43:52Z)
• A Study of Forward-Forward Algorithm for Self-Supervised Learning [65.268245109828]
  We study the performance of forward-forward vs. backpropagation for self-supervised representation learning. Our main finding is that while the forward-forward algorithm performs comparably to backpropagation during (self-supervised) training, the transfer performance is significantly lagging behind in all the studied settings.
  arXiv  Detail & Related papers  (2023-09-21T10:14:53Z)
• Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methods [7.1345443932276424]
  This thesis focuses on representation learning for sequence data over time or space. It aims to improve downstream sequence prediction tasks by using the learned representations.
  arXiv  Detail & Related papers  (2023-07-25T20:38:55Z)
• The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning [32.15608637930748]
  We show that there exists a trade-off between the two desiderata so that one may not be able to achieve both simultaneously. We provide analysis using a theoretical data model and show that, while more diverse pre-training data result in more diverse features for different tasks, it puts less emphasis on task-specific features.
  arXiv  Detail & Related papers  (2023-02-28T22:14:33Z)
• On minimal variations for unsupervised representation learning [19.055611167696238]
  Unsupervised representation learning aims at describing raw data efficiently to solve various downstream tasks. Revealing minimal variations as a guiding principle behind unsupervised representation learning paves the way to better practical guidelines for self-supervised learning algorithms.
  arXiv  Detail & Related papers  (2022-11-07T18:57:20Z)
• Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation [51.21190751266442]
  Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data. By learning from large-scale unlabeled samples, self-supervised learning has now become a new trend in deep learning. We propose a novel textbfSelf-textbfSupervised textbfGraph Neural Network (SSG) to enable more effective inter-task information exchange and knowledge sharing.
  arXiv  Detail & Related papers  (2022-04-08T03:37:56Z)
• Co$^2$L: Contrastive Continual Learning [69.46643497220586]
  Recent breakthroughs in self-supervised learning show that such algorithms learn visual representations that can be transferred better to unseen tasks. We propose a rehearsal-based continual learning algorithm that focuses on continually learning and maintaining transferable representations.
  arXiv  Detail & Related papers  (2021-06-28T06:14:38Z)
• Conditional Meta-Learning of Linear Representations [57.90025697492041]
  Standard meta-learning for representation learning aims to find a common representation to be shared across multiple tasks. In this work we overcome this issue by inferring a conditioning function, mapping the tasks' side information into a representation tailored to the task at hand. We propose a meta-algorithm capable of leveraging this advantage in practice.
  arXiv  Detail & Related papers  (2021-03-30T12:02:14Z)
• Reinforcement Learning with Prototypical Representations [114.35801511501639]
  Proto-RL is a self-supervised framework that ties representation learning with exploration through prototypical representations. These prototypes simultaneously serve as a summarization of the exploratory experience of an agent as well as a basis for representing observations. This enables state-of-the-art downstream policy learning on a set of difficult continuous control tasks.
  arXiv  Detail & Related papers  (2021-02-22T18:56:34Z)
• Learning Invariant Representation for Continual Learning [5.979373021392084]
  A key challenge in Continual learning is catastrophically forgetting previously learned tasks when the agent faces a new one. We propose a new pseudo-rehearsal-based method, named learning Invariant Representation for Continual Learning (IRCL) Disentangling the shared invariant representation helps to learn continually a sequence of tasks, while being more robust to forgetting and having better knowledge transfer.
  arXiv  Detail & Related papers  (2021-01-15T15:12:51Z)
• Predicting What You Already Know Helps: Provable Self-Supervised Learning [60.27658820909876]
  Self-supervised representation learning solves auxiliary prediction tasks (known as pretext tasks) without requiring labeled data. We show a mechanism exploiting the statistical connections between certain em reconstruction-based pretext tasks that guarantee to learn a good representation. We prove the linear layer yields small approximation error even for complex ground truth function class.
  arXiv  Detail & Related papers  (2020-08-03T17:56:13Z)

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.