Ex

Exemplar Networks for a Good Time (NSFW)

Hacker News

Exemplar Networks for a Good Time (NSFW)

http://driftwheeler.com <--- Download APK here. Citation: Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks ( https://arxiv.org/abs/1406.6909 ) 50,000 MetArt-style nudes. No banners, no ads, no hassles, no distractions. Smart image zoom to fit the woman to the screen. See random pics (WANDER). When you like one, see the photoshoot on repeat (TRANCE). To search for something-- e.g., grass, beach, face, pussy-- long press the image (DREAM). This finds nearest neighbors in deep feature space, but only works reliably for simple concepts because it's fully unsupervised. The features you selected by long pressing (inside the box that appears) are more likely, but only very simple searches are "pure". For example, long press a close-up pussy to see more pussies... find one you like, then press TRANCE to see the rest of her. Long press sand and water to see more girls on the beach. Long press forest greenery to see more girls in the forest. Long press a close-up face to see similar faces... And so on. If this is not familiar to you, take a look at: http://cs.stanford.edu/people/karpathy/cnnembed/ Consider one of the big "maps" on that webpage. Notice how the tiny image patches clustered together in a map tend to be similar to each other. When you long press the image in Melondream, a box appears. That box is like one of the tiny image patches. Melondream's DREAM shows you images having patches near the patch you selected, in Melondream's map. Also notice how impure even a supervised dream would be.

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3points
1comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: tiny · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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