SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On
— roadrunner01 (@ak92501) January 20, 2020
pdf: https://t.co/AFinHzzdUa
abs: https://t.co/Pysb1hx6JD pic.twitter.com/mUjEzMO19T
SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On
— roadrunner01 (@ak92501) January 20, 2020
pdf: https://t.co/AFinHzzdUa
abs: https://t.co/Pysb1hx6JD pic.twitter.com/mUjEzMO19T
Fast and reliable face detection with RetinaFace.PyTorch https://t.co/hVUxz4FXRJ #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #pytorch
— PyTorch Best Practices (@PyTorchPractice) January 16, 2020
torchvision v0.5: quantization, production
— PyTorch (@PyTorch) January 16, 2020
- ResNets, MobileNet, ShuffleNet, GoogleNet and InceptionV3 now have quantized counterparts with pre-trained models, scripts for quantization-aware training.
- All models are TorchScript-ready and ONNX-readyhttps://t.co/qbItIUpZFT
How does deep learning perform DEEP learning? Microsoft and CMU researchers establish a principle called "backward feature correction" and explain how very deep neural networks can actually perform DEEP hierarchical learning efficiently: https://t.co/9EtkaThXAT @ZeyuanAllenZhu
— Microsoft Research (@MSFTResearch) January 14, 2020
Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches
— ML Review (@ml_review) January 14, 2020
By @PKU1898 @TAMU
Propose a sketch refinement method using coarse-to-fine dilations, following the drawing process of real artistshttps://t.co/gnghav1lbK pic.twitter.com/gx0YynxBMs
If you are interested in working with doodles and sketches rather than just pixel photos, check out this survey of Deep Learning for Free-Hand Sketch, by Xu et al., with 240 references to what used to be a niche area of machine learning and computer vision https://t.co/hpaFqCzcdT pic.twitter.com/7XaFLM05hx
— hardmaru (@hardmaru) January 14, 2020
been hacking for awhile on the idea of training an embedding based YOLO with self supervision contrastive loss. have never had a good solution for unsupervised object detection piece.... until now!!!
— mat kelcey (@mat_kelcey) January 14, 2020
"Contrastive Learning of Structured World Models" https://t.co/pWJTXdR4SW pic.twitter.com/k2h4I6mkrF
On the Relationship between Self-Attention and Convolutional Layers
— hardmaru (@hardmaru) January 11, 2020
This work shows that attention layers can perform convolution and that they often learn to do so in practice. They also prove that a self-attention layer is as expressive as a conv layer.https://t.co/44I1uOd4LF pic.twitter.com/iqioR9eXzU
Very happy to share our latest work accepted at #ICRL2020: we prove that a Self-Attention layer can express any CNN layer. 1/5
— Jean-Baptiste Cordonnier (@jb_cordonnier) January 10, 2020
📄Paper: https://t.co/Cm61A3PWRA
🍿Interactive website : https://t.co/FTpThM3BQc
🖥Code: https://t.co/xSfmFCy0U2
📝Blog: https://t.co/3bp59RfAcj pic.twitter.com/X1rNS1JvPt
Traffic-Signal-Violation-Detection-System - A Computer Vision based Traffic Signal Violation Detection System from video footage using YOLOv3 & Tkinter. (GUI Included) https://t.co/xlcdJbUPDM #Python #Tkinter #ComputerVision pic.twitter.com/S4cqjGCQIZ
— Python Weekly (@PythonWeekly) January 7, 2020
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Re... https://t.co/QjdnwhbTWY pic.twitter.com/8BBKjKXM67
— arxiv (@arxiv_org) January 5, 2020
64,000 pictures of cars, labeled by make, model, year, price, horsepower, body style, etc. https://t.co/Cjl7CKGvU0
— /MachineLearning (@slashML) January 5, 2020