BigData

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

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

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

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

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

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

How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras

https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/

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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

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

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

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

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

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

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

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

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

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

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

DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation

DaisyRec-v2.0 is a Python toolkit developed for benchmarking top-N recommendation task.

Github: https://github.com/recsys-benchmark/daisyrec-v2.0

Command Generator : http://daisyrecguicommandgenerator.pythonanywhere.com/

Paper: https://arxiv.org/abs/2206.10848v1

Tutorial: https://github.com/recsys-benchmark/DaisyRec-v2.0/blob/main/DaisyRec-v2.0-Tutorial.ipynb

👉 @bigdata_1

Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case

This library implements some of the most common (Variational) Autoencoder models.

Github: https://github.com/clementchadebec/benchmark_VAE

Paper: https://arxiv.org/abs/2206.08309v1

Dataset: https://paperswithcode.com/dataset/celeba

👉 @bigdata_1