Recognizing actions performed in a video requires understanding each frame and relationships between the frames. Previous research devised a way to analyze individual images efficiently known as Active Shift Layer (ASL). New research extends this technique to the steady march of video frames.

A typical GAN’s output doesn’t necessarily reflect the data distribution of its training set. Instead, GANs are prone to modeling the majority of the training distribution, sometimes ignoring rare attributes — say, faces that represent minority populations.

Deepfakes have gone mainstream, allowing celebrities to star in commercials without setting foot in a film studio. A new method helps determine whether such endorsements — and other images produced by generative adversarial networks — are authentic.

GANs are adept at mapping the artistic style of one picture onto the subject of another, known as style transfer. However, applied to the fanciful illustrations in children’s books, some GANs prove better at preserving style, others better at preserving subject matter.

As transformer networks move to the fore in applications from language to vision, the time it takes them to crunch longer sequences becomes a more pressing issue. A new method lightens the computational load using sparse attention.

New research teaches robots to anticipate what’s coming rather than focusing on what’s right in front of them. Researchers developed Occupancy Anticipation (OA), a navigation system that predicts unseen obstacles in addition to observing those in its field of view.

Two years after it pointed a new direction for language models, Bert still hovers near the top of several natural language processing leaderboards. A new study considers whether Bert simply excels at tracking word order or or learns something closer to common sense.

For people with neurological disorders like epilepsy, attaching sensors to the scalp to measure electrical currents within the brain is benign. But interpreting the resulting electroencephalogram (EEG) graphs can give doctors a headache.

Large models pretrained in an unsupervised fashion and then fine-tuned on a smaller corpus of labeled data have achieved spectacular results in natural language processing. New research pushes forward with a similar approach to computer vision.

Everyone has a favorite optimization method, but it’s not always clear which one works best in a given situation. New research aims to establish a set of benchmarks. Researchers evaluated 14 popular optimizers using the Deep Optimization Benchmark Suite some of them introduced last year.

The technique known as dropout discourages neural networks from overfitting by deterring them from reliance on particular features. A new approach reorganizes the process to run efficiently on the chips that typically run neural network calculations.

Transformer networks are gaining popularity as a high-accuracy alternative to recurrent neural networks. But they can run slowly when they’re applied to long sequences.

If the world changes every second and you take a picture every 10 seconds, you won’t have enough pictures to observe the changes clearly, and storing a series of pictures won’t help. On the other hand, if you take a picture every tenth of a second, then storing a history will help model the world.

Semi-supervised learning — a set of training techniques that use a small number of labeled examples and a large number of unlabeled examples — typically treats all unlabeled examples the same way. But some examples are more useful for learning than others.

People who take driving lessons during daytime don’t need instruction in driving at night. They recognize that the difference doesn’t disturb their knowledge of how to drive. Similarly, a new reinforcement learning method manages superficial variations in the environment without re-training.