Semantic Residual Prompts for Continual Learning

• URL: http://arxiv.org/abs/2403.06870v3
• Date: Thu, 18 Jul 2024 10:09:06 GMT
• Title: Semantic Residual Prompts for Continual Learning
• Authors: Martin Menabue, Emanuele Frascaroli, Matteo Boschini, Enver Sangineto, Lorenzo Bonicelli, Angelo Porrello, Simone Calderara,
• Abstract summary: We show that our method significantly outperforms both state-of-the-art CL approaches and the zero-shot CLIP test. Our findings hold true even for datasets with a substantial domain gap w.r.t. the pre-training knowledge of the backbone model.
• Score: 21.986800282078498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt-tuning methods for Continual Learning (CL) freeze a large pre-trained model and train a few parameter vectors termed prompts. Most of these methods organize these vectors in a pool of key-value pairs and use the input image as query to retrieve the prompts (values). However, as keys are learned while tasks progress, the prompting selection strategy is itself subject to catastrophic forgetting, an issue often overlooked by existing approaches. For instance, prompts introduced to accommodate new tasks might end up interfering with previously learned prompts. To make the selection strategy more stable, we leverage a foundation model (CLIP) to select our prompts within a two-level adaptation mechanism. Specifically, the first level leverages a standard textual prompt pool for the CLIP textual encoder, leading to stable class prototypes. The second level, instead, uses these prototypes along with the query image as keys to index a second pool. The retrieved prompts serve to adapt a pre-trained ViT, granting plasticity. In doing so, we also propose a novel residual mechanism to transfer CLIP semantics to the ViT layers. Through extensive analysis on established CL benchmarks, we show that our method significantly outperforms both state-of-the-art CL approaches and the zero-shot CLIP test. Notably, our findings hold true even for datasets with a substantial domain gap w.r.t. the pre-training knowledge of the backbone model, as showcased by experiments on satellite imagery and medical datasets. The codebase is available at https://github.com/aimagelab/mammoth.

Related papers

• Cascade Prompt Learning for Vision-Language Model Adaptation [24.8128345254124]
  Cascade Prompt Learning CasPL is a new learning paradigm comprising two distinct phases of learnable prompts. CasPL can effectively capture both domain-general and task-specific representations into explicitly different gradual groups of prompts. Compared to the previous state-of-the-art method PromptSRC, CasPL shows an average improvement of 1.85% for base classes, 3.44% for novel classes, and 2.72% for the harmonic mean over 11 image classification datasets.
  arXiv  Detail & Related papers  (2024-09-26T12:58:01Z)
• CluMo: Cluster-based Modality Fusion Prompt for Continual Learning in Visual Question Answering [23.360714576158905]
  Large vision-language models (VLMs) have shown significant performance boost in various application domains. Finetuning a VLM on a task leads to reducing its generalization power and the capacity of learning new tasks. We propose a novel prompt-based CL method forVLMs, namely $textbfClu$ster-based $textbfMo$dality Fusion Prompt (textbfCluMo)
  arXiv  Detail & Related papers  (2024-08-21T16:07:49Z)
• CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning [17.614980614656407]
  We propose Continual Generative training for Incremental prompt-Learning. We exploit Variational Autoencoders to learn class-conditioned distributions. We show that such a generative replay approach can adapt to new tasks while improving zero-shot capabilities.
  arXiv  Detail & Related papers  (2024-07-22T16:51:28Z)
• Self-regulating Prompts: Foundational Model Adaptation without Forgetting [112.66832145320434]
  We introduce a self-regularization framework for prompting called PromptSRC. PromptSRC guides the prompts to optimize for both task-specific and task-agnostic general representations.
  arXiv  Detail & Related papers  (2023-07-13T17:59:35Z)
• Exploring Continual Learning for Code Generation Models [80.78036093054855]
  Continual Learning (CL) is an important aspect that remains underexplored in the code domain. We introduce a benchmark called CodeTask-CL that covers a wide range of tasks, including code generation, translation, summarization, and refinement. We find that effective methods like Prompt Pooling (PP) suffer from catastrophic forgetting due to the unstable training of the prompt selection mechanism.
  arXiv  Detail & Related papers  (2023-07-05T16:58:39Z)
• POP: Prompt Of Prompts for Continual Learning [59.15888651733645]
  Continual learning (CL) aims to mimic the human ability to learn new concepts without catastrophic forgetting. We show that a foundation model equipped with POP learning is able to outperform classic CL methods by a significant margin.
  arXiv  Detail & Related papers  (2023-06-14T02:09:26Z)
• LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models [67.19124099815645]
  We propose a novel Language-Aware Soft Prompting (LASP) learning method to alleviate base class overfitting. LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available. LASP matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets.
  arXiv  Detail & Related papers  (2022-10-03T17:56:35Z)
• The CLEAR Benchmark: Continual LEArning on Real-World Imagery [77.98377088698984]
  Continual learning (CL) is widely regarded as crucial challenge for lifelong AI. We introduce CLEAR, the first continual image classification benchmark dataset with a natural temporal evolution of visual concepts. We find that a simple unsupervised pre-training step can already boost state-of-the-art CL algorithms.
  arXiv  Detail & Related papers  (2022-01-17T09:09:09Z)
• Revisiting Deep Local Descriptor for Improved Few-Shot Classification [56.74552164206737]
  We show how one can improve the quality of embeddings by leveraging textbfDense textbfClassification and textbfAttentive textbfPooling. We suggest to pool feature maps by applying attentive pooling instead of the widely used global average pooling (GAP) to prepare embeddings for few-shot classification.
  arXiv  Detail & Related papers  (2021-03-30T00:48:28Z)

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.