Search Results for author:
Found 8 papers, 7 papers with code
Deep Perceptual Similarity is Adaptable to Ambiguous Contexts
The concept of image similarity is ambiguous, and images can be similar in one context and not in another.
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions
Deep perceptual loss is a type of loss function in computer vision that aims to mimic human perception by using the deep features extracted from neural networks.
Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics
This work investigates the most common DPS metric, where deep features are compared by spatial position, along with metrics comparing the averaged and sorted deep features.
Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images
This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors.
Ranked #1 on
Breast Cancer Histology Image Classification
on BreakHis
(Accuracy (Inter-Patient) metric)
Breast Cancer Histology Image Classification (20% labels)
Classification Of Breast Cancer Histology Images
+2
VidHarm: A Clip Based Dataset for Harmful Content Detection
Automatically identifying harmful content in video is an important task with a wide range of applications.
Pretraining Image Encoders without Reconstruction via Feature Prediction Loss
To evaluate this method we perform experiments on three standard publicly available datasets (LunarLander-v2, STL-10, and SVHN) and compare six different procedures for training image encoders (pixel-wise, perceptual similarity, and feature prediction losses; combined with two variations of image and feature encoding/decoding).
Improving Image Autoencoder Embeddings with Perceptual Loss
Autoencoders are trained to embed images from three different computer vision datasets using perceptual loss based on a pretrained model as well as pixel-wise loss.
Subword Semantic Hashing for Intent Classification on Small Datasets
In this paper, we introduce the use of Semantic Hashing as embedding for the task of Intent Classification and achieve state-of-the-art performance on three frequently used benchmarks.
