This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. Other state-of-the-art works mainly focus fully supervised learning approaches that rely heavily human annotations. However, the scarcity of labeled and unlabeled data is long-standing challenge in histopathology. Currently, representation remains unexplored domain. The proposed method, Magnification Prior...

Autoencoders are commonly trained using element-wise loss. However, loss disregards high-level structures in the image which can lead to embeddings that disregard them as well. A recent improvement autoencoders helps alleviate this problem is use of perceptual This work investigates from perspective encoder themselves. embed images three different computer vision datasets based on a pretrained model well pixel-wise host predictors perform object positioning and classification given embedded...

In this paper, we introduce the use of Semantic Hashing as embedding for task Intent Classification and achieve state-of-the-art performance on three frequently used benchmarks. a small dataset is challenging data-hungry Deep Learning based systems. an attempt to overcome such challenge learn robust text classification. Current word are dependent vocabularies. One major drawbacks methods out-of-vocabulary terms, especially when having training datasets using wider vocabulary. This case in...

This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. Other state-of-theart works mainly focus fully supervised learning approaches that rely heavily human annotations. However, the scarcity of labeled and unlabeled data is long-standing challenge in histopathology. Currently, representation remains unexplored for domain. The proposed method, Magnification Prior...

Perturbation-based post-hoc image explanation methods are commonly used to explain prediction models by perturbing parts of the input measure how those affect output. Due intractability each pixel individually, images typically attributed larger segments. The Randomized Input Sampling for Explanations (RISE) method solved this issue using smooth perturbation masks. While has proven effective and popular, it not been investigated which responsible its success. This work tests many...

Measuring the similarity of images is a fundamental problem to computer vision for which no universal solution exists. While simple metrics such as pixel-wise L2-norm have been shown significant flaws, they remain popular. One group recent state-of-the-art that mitigates some those flaws are Deep Perceptual Similarity (DPS) metrics, where evaluated distance in deep features neural networks.However, DPS themselves less thoroughly examined their benefits and, especially, flaws. This work...

Deep perceptual loss is a type of function in computer vision that aims to mimic human perception by using the deep features extracted from neural networks. In recent years, method has been applied great effect on host interesting tasks, especially for tasks with image or image-like outputs, such as synthesis, segmentation, depth prediction, and more. Many applications use pretrained networks, often convolutional calculation. Despite increased interest broader use, more effort needed toward...

This work investigates three methods for calculating loss autoencoder-based pretraining of image encoders: The commonly used reconstruction loss, the more recently introduced deep perceptual similarity and a feature prediction proposed here; latter turning out to be most efficient choice. Standard auto-encoder learning tasks is done by comparing input reconstructed image. Recent shows that predictions based on embeddings generated autoencoders can improved training with i.e., adding network...

Autoencoders are commonly trained using element-wise loss. However, loss disregards high-level structures in the image which can lead to embeddings that disregard them as well. A recent improvement autoencoders helps alleviate this problem is use of perceptual This work investigates from perspective encoder themselves. embed images three different computer vision datasets based on a pretrained model well pixel-wise host predictors perform object positioning and classification given embedded...

The concept of image similarity is ambiguous, and images can be similar in one context not another. This ambiguity motivates the creation metrics for specific contexts. work explores ability deep perceptual (DPS) to adapt a given context. DPS use features neural networks comparing images. These have been successful on datasets that leverage average human perception limited settings. But question remains if they could adapted No single metric suit all contexts, previous rule-based are...

Measuring the similarity of images is a fundamental problem to computer vision for which no universal solution exists. While simple metrics such as pixel-wise L2-norm have been shown significant flaws, they remain popular. One group recent state-of-the-art that mitigates some those flaws are Deep Perceptual Similarity (DPS) metrics, where evaluated distance in deep features neural networks. However, DPS themselves less thoroughly examined their benefits and, especially, flaws. This work...

Automatically identifying harmful content in video is an important task with a wide range of applications. However, there lack professionally labeled open datasets available. In this work VidHarm, dataset 3589 clips from film trailers annotated by professionals, presented. An analysis the performed, revealing among other things relation between clip and trailer level annotations. Audiovisual models are trained on in-depth study modeling choices conducted. The results show that performance...

Automatically identifying harmful content in video is an important task with a wide range of applications. However, there lack professionally labeled open datasets available. In this work VidHarm, dataset 3589 clips from film trailers annotated by professionals, presented. An analysis the performed, revealing among other things relation between clip and trailer level annotations. Audiovisual models are trained on in-depth study modeling choices conducted. The results show that performance...