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Found 9 papers, 2 papers with code
To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?
Critical findings are observed: (1) The best balance between detection accuracy, detection speed and file size is achieved at 8 times downsampling captured with a $40\times$ objective; (2) compression which reduces the file size dramatically, does not necessarily have an adverse effect on overall accuracy; (3) reducing the amount of training data to some extents causes a drop in precision but has a negligible impact on the recall; (4) in most cases, Faster R-CNN achieves the best accuracy in the glomerulus detection task.
Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN
To address this problem, incremental learning methods have been explored which preserve the old knowledge of deep learning models.
SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation
Incremental learning requires a model to continually learn new tasks from streaming data.
Scalable Bayesian Deep Learning with Kernel Seed Networks
To address this problem we present a method for performing BDL, namely Kernel Seed Networks (KSN), which does not require a 2-fold increase in the number of parameters.
DIODE: Dilatable Incremental Object Detection
In this paper, we aim to alleviate this performance decay on multi-step incremental detection tasks by proposing a dilatable incremental object detector (DIODE).
FaceCook: Face Generation Based on Linear Scaling Factors
With the excellent disentanglement properties of state-of-the-art generative models, image editing has been the dominant approach to control the attributes of synthesised face images.
Few-Shot Class-Incremental Learning from an Open-Set Perspective
The continual appearance of new objects in the visual world poses considerable challenges for current deep learning methods in real-world deployments.
Conditioned Generative Transformers for Histopathology Image Synthetic Augmentation
Extensive experiments on the histopathology datasets show that leveraging our synthetic augmentation framework results in significant and consistent improvements in classification performance.
Multivariate Prototype Representation for Domain-Generalized Incremental Learning
Deep learning models suffer from catastrophic forgetting when being fine-tuned with samples of new classes.
