The application of deep learning in cancer prognosis prediction
Deep learning has been applied to many areas in health care, including imaging diagnosis,
digital pathology, prediction of hospital admission, drug design, classification of cancer and …
digital pathology, prediction of hospital admission, drug design, classification of cancer and …
Virtex: Learning visual representations from textual annotations
The de-facto approach to many vision tasks is to start from pretrained visual representations,
typically learned via supervised training on ImageNet. Recent methods have explored …
typically learned via supervised training on ImageNet. Recent methods have explored …
Objects365: A large-scale, high-quality dataset for object detection
In this paper, we introduce a new large-scale object detection dataset, Objects365, which
has 365 object categories over 600K training images. More than 10 million, high-quality …
has 365 object categories over 600K training images. More than 10 million, high-quality …
Nbnet: Noise basis learning for image denoising with subspace projection
In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous
works, we propose to tackle this challenging problem from a new perspective: noise …
works, we propose to tackle this challenging problem from a new perspective: noise …
A corpus for reasoning about natural language grounded in photographs
We introduce a new dataset for joint reasoning about natural language and images, with a
focus on semantic diversity, compositionality, and visual reasoning challenges. The data …
focus on semantic diversity, compositionality, and visual reasoning challenges. The data …
Variational denoising network: Toward blind noise modeling and removal
Blind image denoising is an important yet very challenging problem in computer vision due
to the complicated acquisition process of real images. In this work we propose a new …
to the complicated acquisition process of real images. In this work we propose a new …
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
While there has been remarkable progress in the performance of visual recognition
algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled …
algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled …
Region-based convolutional networks for accurate object detection and segmentation
Object detection performance, as measured on the canonical PASCAL VOC Challenge
datasets, plateaued in the final years of the competition. The best-performing methods were …
datasets, plateaued in the final years of the competition. The best-performing methods were …
Imagenet large scale visual recognition challenge
Abstract The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object
category classification and detection on hundreds of object categories and millions of …
category classification and detection on hundreds of object categories and millions of …
Large loss matters in weakly supervised multi-label classification
Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label
classification using partially observed labels per image, is becoming increasingly important …
classification using partially observed labels per image, is becoming increasingly important …