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Found 16 papers, 5 papers with code
Incremental Few-Shot Meta-Learning via Indirect Discriminant Alignment
This process enables incrementally improving the model by processing multiple learning episodes, each representing a different learning task, even with few training examples.
Semi-supervised Vision Transformers at Scale
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks.
Masked Vision and Language Modeling for Multi-modal Representation Learning
Instead of developing masked language modeling (MLM) and masked image modeling (MIM) independently, we propose to build joint masked vision and language modeling, where the masked signal of one modality is reconstructed with the help from another modality.
X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks
In this paper, we study the challenging instance-wise vision-language tasks, where the free-form language is required to align with the objects instead of the whole image.
Class-Incremental Learning with Strong Pre-trained Models
We hypothesize that a strong base model can provide a good representation for novel classes and incremental learning can be done with small adaptations.
Task Adaptive Parameter Sharing for Multi-Task Learning
TAPS solves a joint optimization problem which determines which layers to share with the base model and the value of the task-specific weights.
Representation Consolidation from Multiple Expert Teachers
Indeed, we observe experimentally that standard distillation of task-specific teachers, or using these teacher representations directly, **reduces** downstream transferability compared to a task-agnostic generalist model.
Representation Consolidation for Training Expert Students
Traditionally, distillation has been used to train a student model to emulate the input/output functionality of a teacher.
A linearized framework and a new benchmark for model selection for fine-tuning
Since all model selection algorithms in the literature have been tested on different use-cases and never compared directly, we introduce a new comprehensive benchmark for model selection comprising of: i) A model zoo of single and multi-domain models, and ii) Many target tasks.
Estimating informativeness of samples with Smooth Unique Information
We define a notion of information that an individual sample provides to the training of a neural network, and we specialize it to measure both how much a sample informs the final weights and how much it informs the function computed by the weights.
End-to-End Piece-Wise Unwarping of Document Images
Document unwarping attempts to undo the physical deformation of the paper and recover a 'flatbed' scanned document-image for downstream tasks such as OCR.
Predicting Training Time Without Training
We tackle the problem of predicting the number of optimization steps that a pre-trained deep network needs to converge to a given value of the loss function.
Rethinking the Hyperparameters for Fine-tuning
Our findings challenge common practices of fine-tuning and encourages deep learning practitioners to rethink the hyperparameters for fine-tuning.
Multi-Task Incremental Learning for Object Detection
For the difficult cases, where the domain gaps and especially category differences are large, we explore three different exemplar sampling methods and show the proposed adaptive sampling method is effective to select diverse and informative samples from entire datasets, to further prevent forgetting.
Incremental Meta-Learning via Indirect Discriminant Alignment
Majority of the modern meta-learning methods for few-shot classification tasks operate in two phases: a meta-training phase where the meta-learner learns a generic representation by solving multiple few-shot tasks sampled from a large dataset and a testing phase, where the meta-learner leverages its learnt internal representation for a specific few-shot task involving classes which were not seen during the meta-training phase.
Few-Shot Learning with Embedded Class Models and Shot-Free Meta Training
We propose a method for learning embeddings for few-shot learning that is suitable for use with any number of ways and any number of shots (shot-free).
