Search Results for author:
Found 18 papers, 8 papers with code
Bayesian Optimization Meets Self-Distillation
On the other hand, Self-Distillation (SD) only transfers partial knowledge learned by the task model itself.
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images.
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date.
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning
However, it often results in selecting a sub-optimal configuration as training with the high-performing configuration typically converges slowly in an early phase.
Interactive Multi-Class Tiny-Object Detection
We perform experiments on the Tiny-DOTA and LCell datasets using both two-stage and one-stage object detection architectures to verify the efficacy of our approach.
Learning Visual Context by Comparison
Current methods for solving this task exploit various characteristics of the chest X-ray image, but one of the most important characteristics is still missing: the necessity of comparison between related regions in an image.
Reducing Domain Gap by Reducing Style Bias
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift.
Ranked #62 on
Domain Generalization
on PACS
Visuomotor Understanding for Representation Learning of Driving Scenes
We postulate that success on this task requires the network to learn semantic and geometric knowledge in the ego-centric view.
PseudoEdgeNet: Nuclei Segmentation only with Point Annotations
In this paper, we propose a weakly supervised nuclei segmentation method, which requires only point annotations for training.
Learning Loss for Active Learning
In this paper, we propose a novel active learning method that is simple but task-agnostic, and works efficiently with the deep networks.
Ranked #3 on
Active Learning
on CIFAR10 (10,000)
Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning
In addition, we present a 'distort-and-recover' training scheme which only requires high-quality reference images for training instead of input and retouched image pairs.
Learning Image Representations by Completing Damaged Jigsaw Puzzles
The recovery of the aforementioned damage pushes the network to obtain robust and general-purpose representations.
Intelligent Assistant for People with Low Vision Abilities
This paper proposes a wearable system for visually impaired people that can be utilized to obtain an extensive feedback about their surrounding environment.
Two-Phase Learning for Weakly Supervised Object Localization
Weakly supervised semantic segmentation and localiza- tion have a problem of focusing only on the most important parts of an image since they use only image-level annota- tions.
Action-Driven Object Detection with Top-Down Visual Attentions
In this paper, we present an "action-driven" detection mechanism using our "top-down" visual attention model.
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
We present a novel detection method using a deep convolutional neural network (CNN), named AttentionNet.
Fisher Kernel for Deep Neural Activations
In this paper, we present a straightforward framework for better image representation by combining the two approaches.
