Random shit Subscribe to Ben Evans. No srsly.Quantum Supremacy Achieved is almost irresistible to print, but it will inevitably mislead the general public. Some explanations wtf ; Speech / audio Spleeter by Deezer - a tool to separate audio tracks. very short abstract ; LPCNet - a vocoder that works 10x faster than WaveGlow … and works wo GPUs: NLP ML CV Why ML in medicine does not work : How JPEG loses data; Very funny over-engineering - Jetson to detect cats http://myplace.frontier.com/~r.bond/cats/cats.htm Unified embedding for visual search at pinterest. All the tasks share a common base network until the embedding is generated, and then things split off into task-specific branches. Task branches are fully connected layers; Sounds like ideas 3 and 5 can be applied to any CV task; Once again stumbled upon this awesome paper by FAIR; Dataset distillation paper and website : Computing Receptive Fields of Convolutional Neural Networks;The Visual Task Adaptation Benchmark ; Google releases pre-trained MobileNet3; CNNs are biased towards texture:If you limit the receptive field (therefore making your CNN rely only on texture), the performance does not drop drastically; Stylized data actually increases performance at test time, even though the testing data is entirely unstylized; 5-shot 5-way miniImageNet test accuracy versus pre-training data composition when using test-time augmentation: Unsupervised Pre-Training of Image Features on Non-Curated Data:Key pains - academic datasets are curated, contain ez clusters of data; All revolves around modes of self-supervision - i.e. rotation, some closeness function, clustering; Labels obtained via hierachical K-means + rotations; Seems to beat vanilla approaches in miniImageNet; They use a very old network - VGG though; Competitions Dockerization in competitions on DD as well?
