Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions

ICLR 2018 Scott ReedYutian ChenThomas PaineAäron van den OordS. M. Ali EslamiDanilo RezendeOriol VinyalsNando de Freitas

Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require many thousands of gradient-based weight updates and unique image examples for training... (read more)

PDF Abstract ICLR 2018 PDF ICLR 2018 Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper