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Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
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Kornia as mentioned in this paper is an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems, such as image transformations, camera calibration, epipolar geometry, and low level image processing techniques.Abstract:
This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems. The package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be inserted inside neural networks to train models to perform image transformations, camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.read more
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Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
TL;DR: The addition of the augmentation method dramatically improves SAC's performance, enabling it to reach state-of-the-art performance on the DeepMind control suite, surpassing model-based methods and recently proposed contrastive learning (CURL).
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TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
TL;DR: Support for 2D, 3D and 4D images such as X-ray, histopathology, CT, ultrasound and diffusion MRI and focus on reproducibility and traceability to encourage open-science practices.
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Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
Krishna Murthy Jatavallabhula,Edward J. Smith,Jean-Francois Lafleche,Clement Fuji Tsang,Artem Rozantsev,Wenzheng Chen,Tommy Xiang,Rev Lebaredian,Sanja Fidler +8 more
TL;DR: Kaolin provides efficient implementations of differentiable 3D modules for use in deep learning systems and curates a comprehensive model zoo comprising many state-of-the-art 3D deep learning architectures to serve as a starting point for future research endeavours.
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DeepEthogram: a machine learning pipeline for supervised behavior classification from raw pixels
James P Bohnslav,Nivanthika K Wimalasena,Kelsey J Clausing,David A Yarmolinsky,Tomás Cruz,eugenia chiappe,Lauren L. Orefice,Clifford J. Woolf,Christopher D. Harvey +8 more
TL;DR: DeepEthogram is software that takes raw pixel values of videos as input and uses machine learning to output an ethogram, the set of user-defined behaviors of interest present in each frame of a video, which is expected to enable the rapid, automated, and reproducible assignment of behavior labels to every frame of an video, thus accelerating all those studies that quantify behaviors ofinterest.
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DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels.
James P Bohnslav,Nivanthika K Wimalasena,Nivanthika K Wimalasena,Kelsey J Clausing,Yu Y Dai,David A Yarmolinsky,David A Yarmolinsky,Tomás Cruz,Adam D Kashlan,Adam D Kashlan,M. Eugenia Chiappe,Lauren L. Orefice,Clifford J. Woolf,Clifford J. Woolf,Christopher D. Harvey +14 more
TL;DR: DeepEthogram as discussed by the authors is a software that uses supervised machine learning to convert raw video pixels into an ethogram, the behaviors of interest present in each video frame, which can be used to quantify researcher-defined behaviors to study neural function, gene mutations, and pharmacological therapies.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.