Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning

• URL: http://arxiv.org/abs/2408.01076v1
• Date: Fri, 2 Aug 2024 07:51:44 GMT
• Title: Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning
• Authors: Lu Yu, Zhe Tao, Hantao Yao, Joost Van de Weijer, Changsheng Xu,
• Abstract summary: Continual learning allows models to learn from new data while retaining previously learned knowledge. The semantic knowledge available in the label information of the images, offers important semantic information that can be related with previously acquired knowledge of semantic classes. We propose integrating semantic guidance within and across tasks by capturing semantic similarity using text embeddings.
• Score: 70.64617500380287
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep neural networks (DNNs) excel on fixed datasets but struggle with incremental and shifting data in real-world scenarios. Continual learning addresses this challenge by allowing models to learn from new data while retaining previously learned knowledge. Existing methods mainly rely on visual features, often neglecting the rich semantic information encoded in text. The semantic knowledge available in the label information of the images, offers important semantic information that can be related with previously acquired knowledge of semantic classes. Consequently, effectively leveraging this information throughout continual learning is expected to be beneficial. To address this, we propose integrating semantic guidance within and across tasks by capturing semantic similarity using text embeddings. We start from a pre-trained CLIP model, employ the \emph{Semantically-guided Representation Learning (SG-RL)} module for a soft-assignment towards all current task classes, and use the Semantically-guided Knowledge Distillation (SG-KD) module for enhanced knowledge transfer. Experimental results demonstrate the superiority of our method on general and fine-grained datasets. Our code can be found in https://github.com/aprilsveryown/semantically-guided-continual-learning.

Related papers

• Enhancing Visual Continual Learning with Language-Guided Supervision [76.38481740848434]
  Continual learning aims to empower models to learn new tasks without forgetting previously acquired knowledge. We argue that the scarce semantic information conveyed by the one-hot labels hampers the effective knowledge transfer across tasks. Specifically, we use PLMs to generate semantic targets for each class, which are frozen and serve as supervision signals.
  arXiv  Detail & Related papers  (2024-03-24T12:41:58Z)
• Online Continual Learning For Interactive Instruction Following Agents [20.100312650193228]
  We argue that such a learning scenario is less realistic since a robotic agent is supposed to learn the world continuously as it explores and perceives it. We propose two continual learning setups for embodied agents; learning new behaviors and new environments.
  arXiv  Detail & Related papers  (2024-03-12T11:33:48Z)
• Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning [63.850451635362425]
  Continual learning requires a model to adapt to ongoing changes in the data distribution. We show that the combination of a large language model and an image generation model can similarly provide useful premonitions. We find that the backbone of our pre-trained networks can learn representations useful for the downstream continual learning problem.
  arXiv  Detail & Related papers  (2024-03-12T06:29:54Z)
• Language Semantic Graph Guided Data-Efficient Learning [10.039953846594805]
  We introduce a Language Semantic Graph (LSG) which is constructed from labels manifest as natural language descriptions. An auxiliary graph neural network is trained to extract high-level semantic relations and then used to guide the training of the primary model. Our in-depth analysis shows that the LSG method also expedites the training process.
  arXiv  Detail & Related papers  (2023-11-15T08:54:57Z)
• COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Tracking [32.47215340215641]
  This paper extends the scope of continual learning research to class-incremental learning for multiple object tracking (MOT) Previous solutions for continual learning of object detectors do not address the data association stage of appearance-based trackers. We introduce COOLer, a COntrastive- and cOntinual-Learning-based tracker, which incrementally learns to track new categories while preserving past knowledge.
  arXiv  Detail & Related papers  (2023-10-04T17:49:48Z)
• Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation [68.13453771001522]
  We propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings. We conduct extensive experiments and evaluate our model on large-scale real-world data.
  arXiv  Detail & Related papers  (2023-06-14T13:07:48Z)
• Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems [22.387120578306277]
  This work aims to advance towards building a state-aware educational recommendation system that incorporates semantic relatedness. We introduce a novel learner model that exploits this semantic relatedness between knowledge components in learning resources using the Wikipedia link graph. Our experiments with a large dataset demonstrate that this new semantic version of TrueLearn algorithm achieves statistically significant improvements in terms of predictive performance.
  arXiv  Detail & Related papers  (2021-12-08T16:23:27Z)
• K-XLNet: A General Method for Combining Explicit Knowledge with Language Model Pretraining [5.178964604577459]
  We focus on improving model pretraining by leveraging explicit knowledge. To be specific, we first match knowledge facts from knowledge graph (KG) and then add a knowledge injunction layer to transformer directly. The experimental results show that solely by adding external knowledge to transformer can improve the learning performance on many NLP tasks.
  arXiv  Detail & Related papers  (2021-03-25T06:14:18Z)
• ALICE: Active Learning with Contrastive Natural Language Explanations [69.03658685761538]
  We propose Active Learning with Contrastive Explanations (ALICE) to improve data efficiency in learning. ALICE learns to first use active learning to select the most informative pairs of label classes to elicit contrastive natural language explanations. It extracts knowledge from these explanations using a semantically extracted knowledge.
  arXiv  Detail & Related papers  (2020-09-22T01:02:07Z)
• Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning [73.0598186896953]
  We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
  arXiv  Detail & Related papers  (2020-04-29T14:22:42Z)

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.