Search Results for author:
Found 11 papers, 5 papers with code
It's All in the Head: Representation Knowledge Distillation through Classifier Sharing
Such direct methods may be limited in transferring high-order dependencies embedded in the representation vectors, or in handling the capacity gap between the teacher and student models.
Ranked #1 on
Face Verification
on IJB-C
ML-Decoder: Scalable and Versatile Classification Head
In this paper, we introduce ML-Decoder, a new attention-based classification head.
Ranked #2 on
Fine-Grained Image Classification
on Stanford Cars
(using extra training data)
Multi-label Classification with Partial Annotations using Class-aware Selective Loss
We propose to estimate the class distribution using a dedicated temporary model, and we show its improved efficiency over a naive estimation computed using the dataset's partial annotations.
Ranked #1 on
Multi-Label Classification
on OpenImages-v6
Semantic Diversity Learning for Zero-Shot Multi-label Classification
We argue that using a single embedding vector to represent an image, as commonly practiced, is not sufficient to rank both relevant seen and unseen labels accurately.
Ranked #3 on
Multi-label zero-shot learning
on Open Images V4
Compact Network Training for Person ReID
Furthermore, we show the representation power of our ReID network via SotA results on a different task of multi-object tracking.
Ranked #16 on
Person Re-Identification
on Market-1501
(Rank-1 metric)
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT
Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results.
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder
Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks.
Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection
Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver.
Anatomical Data Augmentation For CNN based Pixel-wise Classification
In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network.
Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results
In this work we present a novel system for PET estimation using CT scans.
