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Found 11 papers, 5 papers with code
Adaptive Memory Replay for Continual Learning
This continual learning (CL) phenomenon has been extensively studied, but primarily in a setting where only a small amount of past data can be stored.
Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters
We show that STAMINA outperforms the prior SOTA for the setting of text-to-image continual customization on a 50-concept benchmark composed of landmarks and human faces, with no stored replay data.
HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning
In this paper, we attempt to tackle forgetting and heterogeneity while minimizing overhead costs and without requiring access to any stored data.
Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA
We show that C-LoRA not only outperforms several baselines for our proposed setting of text-to-image continual customization, which we refer to as Continual Diffusion, but that we achieve a new state-of-the-art in the well-established rehearsal-free continual learning setting for image classification.
Going Beyond Nouns With Vision & Language Models Using Synthetic Data
We contribute Synthetic Visual Concepts (SyViC) - a million-scale synthetic dataset and data generation codebase allowing to generate additional suitable data to improve VLC understanding and compositional reasoning of VL models.
Ranked #68 on
Visual Reasoning
on Winoground
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning
Our experiments show that we outperform the current SOTA method DualPrompt on established benchmarks by as much as 4. 5% in average final accuracy.
On the Transferability of Visual Features in Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) aims to train a classifier that can generalize to unseen classes, using a set of attributes as auxiliary information, and the visual features extracted from a pre-trained convolutional neural network.
ConStruct-VL: Data-Free Continual Structured VL Concepts Learning
This leads to reasoning mistakes, which need to be corrected as they occur by teaching VL models the missing SVLC skills; often this must be done using private data where the issue was found, which naturally leads to a data-free continual (no task-id) VL learning setting.
FedFOR: Stateless Heterogeneous Federated Learning with First-Order Regularization
For the more typical applications of FL where the number of clients is large (e. g., edge-device and mobile applications), these methods cannot be applied, motivating the need for a stateless approach to heterogeneous FL which can be used for any number of clients.
Incremental Learning with Differentiable Architecture and Forgetting Search
As progress is made on training machine learning models on incrementally expanding classification tasks (i. e., incremental learning), a next step is to translate this progress to industry expectations.
A Closer Look at Rehearsal-Free Continual Learning
Next, we explore how to leverage knowledge from a pre-trained model in rehearsal-free continual learning and find that vanilla L2 parameter regularization outperforms EWC parameter regularization and feature distillation.
