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Found 11 papers, 3 papers with code
Hyperparameter Selection in Continual Learning
In continual learning (CL) -- where a learner trains on a stream of data -- standard hyperparameter optimisation (HPO) cannot be applied, as a learner does not have access to all of the data at the same time.
PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition
In this paper, we further adapt the selective scanning process of Mamba to the visual domain, enhancing its ability to learn features from two-dimensional images by (i) a continuous 2D scanning process that improves spatial continuity by ensuring adjacency of tokens in the scanning sequence, and (ii) direction-aware updating which enables the model to discern the spatial relations of tokens by encoding directional information.
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations
Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data.
Better Practices for Domain Adaptation
However, the domain shift scenario raises a second more subtle challenge: the difficulty of performing hyperparameter optimisation (HPO) for these adaptation algorithms without access to a labelled validation set.
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity
We present a comprehensive evaluation of Parameter-Efficient Fine-Tuning (PEFT) techniques for diverse medical image analysis tasks.
Label-Efficient Object Detection via Region Proposal Network Pre-Training
In this work, we propose a simple pretext task that provides an effective pre-training for the RPN, towards efficiently improving downstream object detection performance.
Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks
We show that learned invariances strongly affect downstream task performance and confirm that different downstream tasks benefit from polar opposite (in)variances, leading to performance loss when the standard augmentation strategy is used.
Self-Supervised Representation Learning: Introduction, Advances and Challenges
Self-supervised representation learning methods aim to provide powerful deep feature learning without the requirement of large annotated datasets, thus alleviating the annotation bottleneck that is one of the main barriers to practical deployment of deep learning today.
How Well Do Self-Supervised Models Transfer?
We evaluate the transfer performance of 13 top self-supervised models on 40 downstream tasks, including many-shot and few-shot recognition, object detection, and dense prediction.
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights
We show that by learning Bayesian instance weights for the unlabelled data, we can improve the downstream classification accuracy by prioritising the most useful instances.
Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights
We show that by learning Bayesian instance weights for the unlabelled data, we can improve the downstream classification accuracy by prioritising the most useful instances.
Ranked #88 on
Image Classification
on STL-10
