SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning
We introduce SoundSpaces 2.0, a platform for on-the-fly geometry-based audio rendering for 3D environments.
Github: https://github.com/facebookresearch/sound-spaces
Paper: https://arxiv.org/abs/2206.08312v1
Dataset: https://paperswithcode.com/dataset/librispeech
👉 @bigdata_1
We introduce SoundSpaces 2.0, a platform for on-the-fly geometry-based audio rendering for 3D environments.
Github: https://github.com/facebookresearch/sound-spaces
Paper: https://arxiv.org/abs/2206.08312v1
Dataset: https://paperswithcode.com/dataset/librispeech
👉 @bigdata_1
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NU-Wave — Official PyTorch Implementation
Github: https://github.com/mindslab-ai/nuwave
Paper: https://arxiv.org/abs/2206.08545v1
Dataset: https://datashare.ed.ac.uk/handle/10283/3443
👉 @bigdata_1
Github: https://github.com/mindslab-ai/nuwave
Paper: https://arxiv.org/abs/2206.08545v1
Dataset: https://datashare.ed.ac.uk/handle/10283/3443
👉 @bigdata_1
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Frequency Dynamic Convolution-Recurrent Neural Network (FDY-CRNN) for Sound Event Detection
Frequency Dynamic Convolution applied kernel that adapts to each freqeuncy bin of input, in order to remove tranlation equivariance of 2D convolution along the frequency axis.
Github: https://github.com/frednam93/FDY-SED
Paper: https://arxiv.org/abs/2206.11645v1
Dataset: https://paperswithcode.com/dataset/desed
👉 @bigdata_1
Frequency Dynamic Convolution applied kernel that adapts to each freqeuncy bin of input, in order to remove tranlation equivariance of 2D convolution along the frequency axis.
Github: https://github.com/frednam93/FDY-SED
Paper: https://arxiv.org/abs/2206.11645v1
Dataset: https://paperswithcode.com/dataset/desed
👉 @bigdata_1
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Retrosynthetic Planning with Retro*
graph-based search policy that eliminates the redundant explorations of any intermediate molecules.
Github: https://github.com/binghong-ml/retro_star
Paper: https://arxiv.org/abs/2206.11477v1
Dataset: https://www.dropbox.com/s/ar9cupb18hv96gj/retro_data.zip?dl=0
👉 @bigdata_1
graph-based search policy that eliminates the redundant explorations of any intermediate molecules.
Github: https://github.com/binghong-ml/retro_star
Paper: https://arxiv.org/abs/2206.11477v1
Dataset: https://www.dropbox.com/s/ar9cupb18hv96gj/retro_data.zip?dl=0
👉 @bigdata_1
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Bi-DexHands: Bimanual Dexterous Manipulation via Reinforcement Learning
Bi-DexHands provides a collection of bimanual dexterous manipulations tasks and reinforcement learning algorithms.
Github: https://github.com/pku-marl/dexteroushands
Isaac Gym: https://developer.nvidia.com/isaac-gym
Paper: https://arxiv.org/abs/2206.08686
👉 @bigdata_1
Bi-DexHands provides a collection of bimanual dexterous manipulations tasks and reinforcement learning algorithms.
Github: https://github.com/pku-marl/dexteroushands
Isaac Gym: https://developer.nvidia.com/isaac-gym
Paper: https://arxiv.org/abs/2206.08686
👉 @bigdata_1
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SETR - Pytorch
Github: https://github.com/920232796/setr-pytorch
Paper: https://arxiv.org/abs/2206.11520v1
Dataset: https://www.kaggle.com/c/carvana-image-masking-challenge/data
👉 @bigdata_1
Github: https://github.com/920232796/setr-pytorch
Paper: https://arxiv.org/abs/2206.11520v1
Dataset: https://www.kaggle.com/c/carvana-image-masking-challenge/data
👉 @bigdata_1
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MindWare: Efficient Open-source AutoML System
MindWare is an efficient open-source system to help users to automate the process of: 1) data pre-processing, 2) feature engineering, 3) algorithm selection, 4) architecture design, 5) hyper-parameter tuning, and 6) model ensembling.
Github: https://github.com/PKU-DAIR/mindware
Docs: https://mindware.readthedocs.io/en/latest/
Paper: https://arxiv.org/abs/2206.09423v1
👉 @bigdata_1
MindWare is an efficient open-source system to help users to automate the process of: 1) data pre-processing, 2) feature engineering, 3) algorithm selection, 4) architecture design, 5) hyper-parameter tuning, and 6) model ensembling.
Github: https://github.com/PKU-DAIR/mindware
Docs: https://mindware.readthedocs.io/en/latest/
Paper: https://arxiv.org/abs/2206.09423v1
👉 @bigdata_1
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Tntorch - Tensor Network Learning with PyTorch
PyTorch-powered modeling and learning library using tensor networks. Installation: pip install tntorch
Github: https://github.com/rballester/tntorch
Docs site: http://tntorch.readthedocs.io/
Paper: https://arxiv.org/abs/2206.11128v1
👉 @bigdata_1
PyTorch-powered modeling and learning library using tensor networks. Installation: pip install tntorch
Github: https://github.com/rballester/tntorch
Docs site: http://tntorch.readthedocs.io/
Paper: https://arxiv.org/abs/2206.11128v1
👉 @bigdata_1
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Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech
IMS Toucan is a toolkit for teaching, training and using state-of-the-art Speech Synthesis models.
Github: https://github.com/rballester/tntorch
Pre-Generated Audios: https://multilingualtoucan.github.io/
Cloning prosody across speakers: https://toucanprosodycloningdemo.github.io/
Paper: https://arxiv.org/abs/2206.12229v1
👉 @bigdata_1
IMS Toucan is a toolkit for teaching, training and using state-of-the-art Speech Synthesis models.
Github: https://github.com/rballester/tntorch
Pre-Generated Audios: https://multilingualtoucan.github.io/
Cloning prosody across speakers: https://toucanprosodycloningdemo.github.io/
Paper: https://arxiv.org/abs/2206.12229v1
👉 @bigdata_1
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Insubstantial Object Detection
Dataset comprised of 600 videos (141,017 frames) covering various distances, sizes, visibility, and scenes captured by different spectral ranges.
Github: https://github.com/calayzhou/iod-video
Project: https://calayzhou.github.io/
Paper: https://arxiv.org/abs/2206.11459v1
Dataset: https://paperswithcode.com/dataset/coco
👉 @bigdata_1
Dataset comprised of 600 videos (141,017 frames) covering various distances, sizes, visibility, and scenes captured by different spectral ranges.
Github: https://github.com/calayzhou/iod-video
Project: https://calayzhou.github.io/
Paper: https://arxiv.org/abs/2206.11459v1
Dataset: https://paperswithcode.com/dataset/coco
👉 @bigdata_1
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Forecasting Future World Events with Neural Networks
Github: https://github.com/andyzoujm/autocast
Paper: https://arxiv.org/abs/2206.15474v1
Dataset: https://people.eecs.berkeley.edu/~hendrycks/intervalqa.tar.gz
👉 @bigdata_1
Github: https://github.com/andyzoujm/autocast
Paper: https://arxiv.org/abs/2206.15474v1
Dataset: https://people.eecs.berkeley.edu/~hendrycks/intervalqa.tar.gz
👉 @bigdata_1
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How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras
https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/
👉 @bigdata_1
https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/
👉 @bigdata_1
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A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges
Github: https://github.com/shiqiyu/opengait
Paper: https://arxiv.org/abs/2206.13732v1
Dataset: https://paperswithcode.com/dataset/usf
👉 @bigdata_1
Github: https://github.com/shiqiyu/opengait
Paper: https://arxiv.org/abs/2206.13732v1
Dataset: https://paperswithcode.com/dataset/usf
👉 @bigdata_1
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Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutions
Github: https://github.com/Deep-MI/FastSurfer
Colab: https://colab.research.google.com/github/Deep-MI/FastSurfer/blob/master/Tutorial/Tutorial_FastSurferCNN_QuickSeg.ipynb
Paper: https://arxiv.org/abs/2206.14919v1
👉 @bigdata_1
Github: https://github.com/Deep-MI/FastSurfer
Colab: https://colab.research.google.com/github/Deep-MI/FastSurfer/blob/master/Tutorial/Tutorial_FastSurferCNN_QuickSeg.ipynb
Paper: https://arxiv.org/abs/2206.14919v1
👉 @bigdata_1
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FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
Github: https://github.com/timothyhtimothy/fast-vqa
Paper: https://arxiv.org/abs/2207.02595v1
Dataset: https://paperswithcode.com/dataset/kinetics
👉 @bigdata_1
Github: https://github.com/timothyhtimothy/fast-vqa
Paper: https://arxiv.org/abs/2207.02595v1
Dataset: https://paperswithcode.com/dataset/kinetics
👉 @bigdata_1
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Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations
For the first time brings the power of robust data augmentations into regularizing the NeRF training.
Github: https://github.com/vita-group/aug-nerf
Paper: https://arxiv.org/abs/2207.01164v1
Cloud Drive: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
👉 @bigdata_1
For the first time brings the power of robust data augmentations into regularizing the NeRF training.
Github: https://github.com/vita-group/aug-nerf
Paper: https://arxiv.org/abs/2207.01164v1
Cloud Drive: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
👉 @bigdata_1
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Bounding Box Deep Learning: The Future of Video Annotation
https://www.kdnuggets.com/2022/07/bounding-box-deep-learning-future-video-annotation.html
👉 @bigdata_1
https://www.kdnuggets.com/2022/07/bounding-box-deep-learning-future-video-annotation.html
👉 @bigdata_1
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SeqDeepFake: Detecting and Recovering Sequential DeepFake Manipulation
First Seq-DeepFake dataset, where face images are manipulated sequentially with corresponding annotations of sequential facial manipulation vectors.
Github: https://github.com/rshaojimmy/seqdeepfake
Project: https://rshaojimmy.github.io/Projects/SeqDeepFake
Paper: https://arxiv.org/pdf/2207.02204.pdf
Dataset: https://paperswithcode.com/dataset/imagenet
👉 @bigdata_1
First Seq-DeepFake dataset, where face images are manipulated sequentially with corresponding annotations of sequential facial manipulation vectors.
Github: https://github.com/rshaojimmy/seqdeepfake
Project: https://rshaojimmy.github.io/Projects/SeqDeepFake
Paper: https://arxiv.org/pdf/2207.02204.pdf
Dataset: https://paperswithcode.com/dataset/imagenet
👉 @bigdata_1
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Understanding the Design of a Convolutional Neural Network
https://machinelearningmastery.com/understanding-the-design-of-a-convolutional-neural-network/
👉 @bigdata_1
https://machinelearningmastery.com/understanding-the-design-of-a-convolutional-neural-network/
👉 @bigdata_1
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YOLOv7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Github: https://github.com/wongkinyiu/yolov7
Paper: https://arxiv.org/abs/2207.02696v1
Dataset: https://paperswithcode.com/dataset/coco
👉 @bigdata_1
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Github: https://github.com/wongkinyiu/yolov7
Paper: https://arxiv.org/abs/2207.02696v1
Dataset: https://paperswithcode.com/dataset/coco
👉 @bigdata_1
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