Few-shot Learning with a Universal Template (FLUTE)
ResNet

Pattern for Efficient Learning

Getting high accuracy out of a classifier trained on a small number of examples is tricky. You might train the model on several large-scale datasets prior to few-shot training, but what if the few-shot dataset includes novel classes? A new method performs well even in that case.
2 min read
Computer vision is probing the history of ancient pottery
ResNet

Sorting Shattered Traditions

Computer vision is probing the history of ancient pottery | What’s new: Researchers at Northern Arizona University developed a machine learning model that identifies different styles of Native American painting on ceramic fragments and sorts the shards by historical period.
1 min read
Two images showing the process of turning handwriting into text
ResNet

The Writing, Not the Doodles

Systems designed to turn handwriting into text typically work best on pages with a consistent layout, such as a single column unbroken by drawings, diagrams, or extraneous symbols. A new system removes that requirement.
2 min read
Architecture of vision-language tasks
ResNet

One Model for Vision-Language

Researchers have proposed task-agnostic architectures for image classification tasks and language tasks. New work proposes a single architecture for vision-language tasks.
2 min read
Model identifying erroneous labels in popular datasets
ResNet

Labeling Errors Everywhere

Key machine learning datasets are riddled with mistakes. Several benchmark datasets are shot through with incorrect labels. On average, 3.4 percent of examples in 10 commonly used datasets are mislabeled and the detrimental impact of such errors rises with model size.
2 min read
Data related to SElf-supERvised (SEER), an image classifier pretrained on uncurated, unlabeled images
ResNet

Pretraining on Uncurated Data

It’s well established that pretraining a model on a large dataset improves performance on fine-tuned tasks. In sufficient quantity and paired with a big model, even data scraped from the internet at random can contribute to the performance boost.
2 min read
Sequence related to image processing
ResNet

Vision Models Get Some Attention

Self-attention is a key element in state-of-the-art language models, but it struggles to process images because its memory requirement rises rapidly with the size of the input. New research addresses the issue with a simple twist on a convolutional neural network.
2 min read
Sequence showing a training step that uses different perspectives of the same patient to enhance unsupervised pretraining
ResNet

Same Patient, Different Views

When you lack labeled training data, pretraining a model on unlabeled data can compensate. New research pretrained a model three times to boost performance on a medical imaging task.
2 min read
Graphs and data related to ReLabel, a technique that labels any random crop of any image.
ResNet

Good Labels for Cropped Images

In training an image recognition model, it’s not uncommon to augment the data by cropping original images randomly. But if an image contains several objects, a cropped version may no longer match its label. Researchers developed a way to make sure random crops are labeled properly.
2 min read
Graphs and data related to ImageNet performance
ResNet

ImageNet Performance: No Panacea

It’s commonly assumed that models pretrained to achieve high performance on ImageNet will perform better on other visual tasks after fine-tuning. But is it always true? A new study reached surprising conclusions.
2 min read
System Oscar+ working
ResNet

Sharper Eyes For Vision+Language

Models that interpret the interplay of words and images tend to be trained on richer bodies of text than images. Recent research worked toward giving such models a more balanced knowledge of the two domains.
2 min read
Different data related to the phenomenon called underspecification
ResNet

Facing Failure to Generalize

The same models trained on the same data may show the same performance in the lab, and yet respond very differently to data they haven’t seen before. New work finds this inconsistency to be pervasive.
2 min read
Art pieces with subjective commentary regarding their emotional impact
ResNet

How Art Makes AI Feel

An automated art critic spells out the emotional impact of images. Led by Panos Achlioptas, researchers at Ecole Polytechnique, King Abdullah University, and Stanford University trained a deep learning system to generate subjective interpretations of art.
2 min read
Series of images and graphs related to cancer detection
ResNet

Shortcut to Cancer Treatment

Doctors who treat breast cancer typically use a quick, inexpensive tumor-tissue stain test to diagnose the illness and a slower, more costly one to determine treatment. A new neural network could help doctors to go straight from diagnosis to treatment.
2 min read
ResNet

Pain Points in Black and White

A model designed to assess medical patients’ pain levels matched the patients’ own reports better than doctors’ estimates did — when the patients were Black.
1 min read

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