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Found 7 papers, 3 papers with code
Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves.
Continental-Scale Building Detection from High Resolution Satellite Imagery
Identifying the locations and footprints of buildings is vital for many practical and scientific purposes.
Representation learning from videos in-the-wild: An object-centric approach
We propose a method to learn image representations from uncurated videos.
Self-Supervised Learning of Video-Induced Visual Invariances
We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI).
Ranked #14 on
Image Classification
on VTAB-1k
(using extra training data)
High-Fidelity Image Generation With Fewer Labels
Deep generative models are becoming a cornerstone of modern machine learning.
Ranked #7 on
Conditional Image Generation
on ImageNet 128x128
Conditional Image Generation
Vocal Bursts Intensity Prediction
Self-Supervised GANs via Auxiliary Rotation Loss
In this work we exploit two popular unsupervised learning techniques, adversarial training and self-supervision, and take a step towards bridging the gap between conditional and unconditional GANs.
Ranked #6 on
Image Generation
on CelebA-HQ 128x128
Now Playing: Continuous low-power music recognition
To reduce battery consumption, a small music detector runs continuously on the mobile device's DSP chip and wakes up the main application processor only when it is confident that music is present.
