Image processing technique explained
Efficiency

Preserving Detail in Image Inputs

Given real-world constraints on memory and processing time, images are often downsampled before they’re fed into a neural network. But the process removes fine details, and that degrades accuracy. A new technique squeezes images with less compromise.
2 min read
Graphs related to double descent
Efficiency

Moderating the ML Roller Coaster

Wait a minute — we added training data, and our model’s performance got worse?! New research offers a way to avoid so-called double descent.
2 min read
Data and graphs related to equations that optimize some training parameters.
Efficiency

Optimize Your Training Parameters

Last week we reported on a formula to determine model width and dataset size for optimal performance. A new paper contributes equations that optimize some training parameters.
2 min read
Data related to Reformer
Efficiency

Transformers Transformed

Transformer networks have revolutionized natural language processing, but they hog processor cycles and memory. New research demonstrates a more frugal variation.
2 min read
EfficientDet explained
Efficiency

Easy on the Eyes

Researchers aiming to increase accuracy in object detection generally enlarge the network, but that approach also boosts computational cost. A novel architecture sets a new state of the art in accuracy while cutting the compute cycles required.
2 min read
OctConv example
Efficiency

Convolution Revolution

Looking at images, people see outlines before the details within them. A replacement for the traditional convolutional layer decomposes images based on this distinction between coarse and fine features.
2 min read
An illustration of filter pruning
Efficiency

High Accuracy, Low Compute

As neural networks have become more accurate, they’ve also ballooned in size and computational cost. That makes many state-of-the-art models impractical to run on phones and potentially smaller, less powerful devices.
2 min read
DeepScale's automated vehicle technology
Efficiency

Tesla Bets on Slim Neural Nets

Elon Musk has promised a fleet of autonomous Tesla taxis by 2020. The company reportedly purchased a computer vision startup to help meet that goal. Tesla acquired DeepScale, a Silicon Valley startup that rocesses computer vision on low-power electronics.
1 min read
Illustration of Facebook AI Research method to compress neural networks
Efficiency

Honey, I Shrunk the Network!

Deep learning models can be unwieldy and often impractical to run on smaller devices without major modification. Researchers at Facebook AI Research found a way to compress neural networks with minimal sacrifice in accuracy.
2 min read

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