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Our Local Aggregation work is 1 of 7
#ICCV2019 Best Paper Award Nominees! We achieve SOTA results in unsupervised learning. We have also released a pytorch version at https://github.com/neuroailab/LocalAggregation-Pytorch … (great thanks to@mike_h_wu and @honglinc_ ). Paper and TF-codes are in my previous post.Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
We achieve SOTA results on unsupervised learning benchmark, surpassing previous methods by large margins. We hope it can help further bridge the gap between un- and supervised learning. We also want to better understand how human learns through these algorithms.https://twitter.com/dyamins/status/1157394063500296192 …
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Really nice work! Wonder how it relates to deep embedding approach. actually we got > alexnet a couple mos. ago with much shallower network (resnet50). https://arxiv.org/abs/1903.12355 curious how we’re going to get close to supervised numbers without extra depth.
@arkitus@dyaminshttps://twitter.com/arkitus/status/1131462115456376833 …
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Chengxu Zhuang proslijedio/la je Tweet
unsupervised learning is the missing cake under the icing of supervision. zhuang, zhai &
@dyamins describe a method for deep learning of locally clustered embeddings (https://arxiv.org/pdf/1903.12355.pdf …) and henaff, al., et@avdnoord use spatial contrastive predictive coding...https://twitter.com/arkitus/status/1131462115456376833 …
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Excited to show our new results on unsupervised learning!https://twitter.com/dyamins/status/1116058740443107328 …
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