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Newmu
dcgan_code
Python2015–2017
Newmu / dcgan_code

dcgan_code

Deep Convolutional Generative Adversarial Networks

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12Distinct people
14Mentions
0Explicit recommenders
3.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Deep Convolutional Generative Adversarial Networks

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 12:01:02 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month14 dated mentions

Observed mentions: 2015-11-23 17:52:31 UTC to 2017-07-10 15:21:19 UTC.

0123452015-11-01 00:00:00 UTC to 2015-12-01 00:00:00 UTC (exclusive): 5 mentions; 4 submissions, 1 commentsNov 20152015-12-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2016-01-01 00:00:00 UTC to 2016-02-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2016-02-01 00:00:00 UTC to 2016-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-03-01 00:00:00 UTC to 2016-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2016-04-01 00:00:00 UTC to 2016-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-05-01 00:00:00 UTC to 2016-06-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2016-06-01 00:00:00 UTC to 2016-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2016-07-01 00:00:00 UTC to 2016-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-08-01 00:00:00 UTC to 2016-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-09-01 00:00:00 UTC to 2016-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsSep 20162016-10-01 00:00:00 UTC to 2016-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2016-11-01 00:00:00 UTC to 2016-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-12-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-01-01 00:00:00 UTC to 2017-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-02-01 00:00:00 UTC to 2017-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-03-01 00:00:00 UTC to 2017-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-04-01 00:00:00 UTC to 2017-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-05-01 00:00:00 UTC to 2017-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-06-01 00:00:00 UTC to 2017-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-07-01 00:00:00 UTC to 2017-08-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJul 2017

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Nov 20152015-11-01 00:00:00 UTC2015-12-01 00:00:00 UTC541
Dec 20152015-12-01 00:00:00 UTC2016-01-01 00:00:00 UTC101
Jan 20162016-01-01 00:00:00 UTC2016-02-01 00:00:00 UTC202
Feb 20162016-02-01 00:00:00 UTC2016-03-01 00:00:00 UTC000
Mar 20162016-03-01 00:00:00 UTC2016-04-01 00:00:00 UTC101
Apr 20162016-04-01 00:00:00 UTC2016-05-01 00:00:00 UTC000
May 20162016-05-01 00:00:00 UTC2016-06-01 00:00:00 UTC202
Jun 20162016-06-01 00:00:00 UTC2016-07-01 00:00:00 UTC101
Jul 20162016-07-01 00:00:00 UTC2016-08-01 00:00:00 UTC000
Aug 20162016-08-01 00:00:00 UTC2016-09-01 00:00:00 UTC000
Sep 20162016-09-01 00:00:00 UTC2016-10-01 00:00:00 UTC000
Oct 20162016-10-01 00:00:00 UTC2016-11-01 00:00:00 UTC101
Nov 20162016-11-01 00:00:00 UTC2016-12-01 00:00:00 UTC000
Dec 20162016-12-01 00:00:00 UTC2017-01-01 00:00:00 UTC000
Jan 20172017-01-01 00:00:00 UTC2017-02-01 00:00:00 UTC000
Feb 20172017-02-01 00:00:00 UTC2017-03-01 00:00:00 UTC000
Mar 20172017-03-01 00:00:00 UTC2017-04-01 00:00:00 UTC000
Apr 20172017-04-01 00:00:00 UTC2017-05-01 00:00:00 UTC000
May 20172017-05-01 00:00:00 UTC2017-06-01 00:00:00 UTC000
Jun 20172017-06-01 00:00:00 UTC2017-07-01 00:00:00 UTC000
Jul 20172017-07-01 00:00:00 UTC2017-08-01 00:00:00 UTC101

What Hacker News says

2 sources
deepnet·May 3, 2016
A Hinton inspired speculation about a Robot that plans & communicates. I posit that general A.I. must be embodied to develop, it must learn from the real world by interacting with it and learn by trial and error. Deep learning is a huge leap for AI. It learns best from raw data. Babies learn motor control & input interpretation first[1]. Moravec's paradox proposes learning these basics are the hard foundation that higher levels of reason build on. Abeel and Levine's work[2] where robots learn robust functions to map from pixels to motor torques for domestic tasks show solutions built on features learnt from raw data are more resilient than hand-engineered ones. These trajectories through motor-torque space to produce a desired goal are intriguingly like planning. Mikolov's[3] Word2vec & Radford's Image Vectors[4] produce semantic spaces w
Read the rest
here vector math is akin to reasoning. Higher level control could be achieved through vector algebras deriving actions and things directly from the robots internal vector space of motor-torques and raw-pixels Thus internal vectors are like thoughts[5] which can interpret, reason, plan, and control. Karpathy shows internal vectors can bridge modalities from words to pictures[6]. If the higher level control vectors of actions and things could translate to the modality of language as verbs and nouns, perhaps the robot could discuss its plans, and receive orders. [1] - Thelen's Infant Development http://www.ncbi.nlm.nih.gov/pubmed/17107442 [2] - Levine, Finn, Darrel, Abeel http://arxiv.org/abs/1504.00702 & [2b] - Distributed Grasp Learning http://googleresearch.blogspot.co.uk/2016/03/deep-learning-f... [3] - Mikolov, Chen, Corrado, Dean http://arxiv.org/abs/1301.3781 [4] - Radford, Metz, Chintala https://github.com/Newmu/dcgan_code#arithmetic-on-faces [5] - Hinton https://youtu.be/IcOMKXAw5VA?t=32m55s [6] - Karpathy https://youtu.be/ZkY7fAoaNcg?t=38m31s
In My path to OpenAI

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