[HN Gopher] Launch HN: Lightly (YC S21): Label only the data whi...
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Launch HN: Lightly (YC S21): Label only the data which improves
your ML model
Hi HackerNews! We're Matt and Igor from Lightly
(https://www.lightly.ai/). Most companies that do machine learning
at scale label only 1% of their data because it's too expensive to
label all of it. We built Lightly to help companies pick the most
valuable 1% to be labeled. If you wonder what data labeling looks
like for images then think about these captchas that want you to
tag images in the web containing objects such as a bus or person.
When we were working on training machine learning (ML) models from
scratch, we often had to do this labeling ourselves. But there was
always far too much data for us to be able to label all of it. We
talked with more than 250 ML teams ranging from small groups of 2-3
people to large teams at Apple and Google, and they all face the
same problem: they have too much data to label. Not only that, but
there wouldn't be a lot of value in labeling everything. For
example, if you have billions of images, it's a waste of time to
get humans to label every one of them, because most of those labels
wouldn't add useful information to the model you're hoping to
train. Most of the images are probably similar enough to other
images that have already been labeled and they have nothing new to
tell your model. Spending more labeling effort on those would be a
bit like labeling the same image over and over again--quite
wasteful. As soon as your ML model surpasses the initial prototype
stage, you're most interested in the edge cases in your dataset --
the ones that represent rare events. For example, a few days ago,
there was a Twitter thread about failure cases for Tesla vehicles.
One Tesla has mistaken a yellow moon for a yellow traffic light:
https://twitter.com/JordanTeslaTech/status/14184133078625853....
Another edge case is a truck full of traffic lights:
https://twitter.com/haltakov/status/1400797882891091970. Finding
and labeling such rare cases is key to having a robust system that
will work in difficult situations. Rather than labeling
everything, a better approach is to first discard all the redundant
images and keep only the ones that it's worth spending time/money
to label. Let's call those "interesting" images. If you could spend
labeling effort only on the "interesting" images, you'd get the
same value for a fraction of the cost. Many ML companies in a more
advanced stage have had to tackle this problem. One approach is to
pay people to go through the images and discard the "boring"
(nothing-new-to-tell-me) images, leaving the "interesting" (worth-
spending-resources-to-label) ones. That can save you money if it's
on average cheaper to answer the question "boring or interesting?"
about an image than it is to label it. This solution scales as long
as you have an increasing human labeling workforce every year.
However, ML data doubles every year on average, and therefore the
labeling capacity would need to double too. Much better than that
-- the holy grail -- would be for a computer to do the work of
discarding the "boring" images. Compared to paying humans to do it,
you'd get the "interesting" subset of your billion images almost
for free. You would have much less work to do (or money to spend)
on labeling, and you'd get just as good a model after training. You
could split the savings with whoever knew how to make a computer do
this for you, and you'd both come out ahead. That's basically our
intention with Lightly. My co-founder Matt and I worked on many
machine learning projects ourselves, where we also had to manage
tooling and annotation budgets. Dealing with data in a production
environment is different from academia. In academia, we have well-
balanced and manually curated datasets. It is, as some of you know,
a huge pain. The solution of the problem boils down to working with
unlabeled data. Luckily, in recent years, a new subfield of deep
learning has emerged called self-supervised learning. It's a
technique to train models to understand data without any labels. In
natural language processing (NLP), modern models like BERT or GPT
all rely on it. In computer vision, we have had a similar
breakthrough in the last year with models such as SimCLR or MoCo.
Back in 2020, we started experimenting with self-supervised
learning to better understand unlabeled data and improve our
software. However, there was no easy-to-use framework available to
work with the latest models. To solve that problem, we built our
own framework to make the power of self-supervised learning easily
accessible. Since we want to foster research in this domain and
grow a bigger community around this topic we decided to open-source
the framework in fall 2020 (https://github.com/lightly-ai/lightly).
It is now used by universities and research labs all over the
world. We realized that the ability to understand and visualize
unlabeled data is also valuable to other ML teams and decided to
offer our solution as a SaaS platform. The platform builds on the
open-source framework and helps you work with the most valuable
data. Here are some examples where Lightly can help you: Analyze
the quality and diversity of your datasets. Our platform can also
use metadata or labels if available. Uncover class distributions,
dataset gaps, and representation biases before labeling to save
time and money. You can do it manually or automated through our
data selection algorithms which ensure the most diverse subset of
your dataset is chosen. (https://docs.lightly.ai/). Once you have
a labeled dataset and trained your model, our active-learning
algorithms allow you to gradually select the next data to be added
to your training set. Only label the best data for model training
until you reach your target accuracy.
https://docs.lightly.ai/getting_started/active_learning.html Check
it out yourself with this quick demo video
https://www.youtube.com/watch?v=38kwv0xEIz4 Lightly integrates
with an API directly into your pipeline, is available on-prem, and
can process up to 100M samples within hours. We're excited we get
to show Lightly to you all. Thank you for reading! Please let us
know your thoughts and questions in the comments.
Author : isusmelj
Score : 69 points
Date : 2021-08-09 13:17 UTC (9 hours ago)
| henning wrote:
| Since I don't do machine learning I don't know whether your
| product is good or not, but I automatically appreciate you
| calling this "machine learning" and not "AI".
| isusmelj wrote:
| Thanks, we also try to avoid using AI as much as possible :)
| dpaleka wrote:
| How do models trained with Lightly compare with other approaches
| wrt adversarial robustness?
|
| Can using Lightly introduce additional bias in the model, since
| only a select few of inputs are being labeled? This may be a
| concern for publicity purposes.
|
| By the way, I thought ETH spinoff requirements were incompatible
| with YC requirements - nice to see it can be made to work.
| isusmelj wrote:
| Thanks for the interest and great questions. Responses are
| below:
|
| >How do models trained with Lightly compare with other
| approaches wrt adversarial robustness?
|
| We have no benchmark available. Both approaches can be
| combined. You can use Lightly to pick a diverse subset, label
| it and then during training/ evaluating the model check for
| adversarial robustness and re-iterate.
|
| >Can using Lightly introduce additional bias in the model,
| since only a select few of inputs are being labeled? This may
| be a concern for publicity purposes.
|
| If we remove bias we automatically introduce bias. BUT we want
| the introduced bias to be controlled and known.
|
| Bias typically comes from the way we collect data. For example,
| more data is being collected during the day than during night
| for autonomous driving. We also have more data collected during
| sunny weather than rain or snow. We also have more data from
| cities like San Fransisco than cities like New Mexico. Most of
| our datasets are biased.
|
| > By the way, I thought ETH spinoff requirements were
| incompatible with YC requirements - nice to see it can be made
| to work.
|
| From what we know we are the first ETH spin-off who is part of
| the YC program. we hope they don't abandon us.
| dragosbulugean wrote:
| > We built Lightly to help companies pick the most valuable 1% to
| be labeled.
|
| Awesome!
| isusmelj wrote:
| Thanks!
| citilife wrote:
| Having built a model to identify sensitive data having a solid
| data labeling solution would be awesome. I can attest this is a
| real problem. Here's the library we built:
|
| https://github.com/capitalone/DataProfiler
|
| In this space, Prodigy really dominates:
|
| https://prodi.gy/
|
| We actually built our own internal system which integrates and
| can export the labels (does predictive labeling, etc). Of course,
| we only focused on text data at the moment.
|
| All that being said, this is going to become a crowded space and
| highly competitive. Plus, once the data is labeled companies
| often drop their labelers. I would recommend ensuring some
| consistent use. Potentially, hosting their models off-prem or
| something to lock companies in.
| isusmelj wrote:
| The library looks great!
|
| Prodi.gy is great but focuses heavily on NLP and speeding up
| the labeling process. Our goal is really to help you reduce
| what you label before you use any labeling tool.
|
| We are working with labeling tools as well as providers to
| streamline the workflow.
| tracyhenry wrote:
| Doesn't Prodi.gy also claim to do active learning, which
| essentially reduce the instances to label too?
|
| Haven't used Prodi.gy so don't know how its active learning
| algo works. May you share the difference?
| isusmelj wrote:
| Most active learning frameworks just use model predictions
| to find the images where the model has the lowest
| confidence and ignore the image diversity aspect. E.g. the
| model struggles with bicycles at night. The problem with
| this approach is that you might end up adding many new
| images to your labeling pipeline that are very similar to
| each other.
|
| However, with Lightly you can additionally make sure you
| only select images that are visually different from each
| other. And you always get visual feedback on the selected
| data in our web platform. The additional control and
| feedback mechanism allow you to have a more focused
| workflow.
| RicoElectrico wrote:
| How does it differentiate from modAL? I think at a glance they
| try to achieve roughly the same thing: give human labelers only
| the datapoints most relevant to the problem at hand.
|
| https://github.com/modAL-python/modAL
| MalteEbner wrote:
| modAL indeed has a similar goal of choosing the best subset of
| data to be labeled. However it has some notable differences:
|
| It is built on scikit-learn which is also evident from the
| suggested workflow. Lightly on the other hand was specifically
| built for deep learning applications supporting active learning
| for classification but also object detection and semantic
| segmentation.
|
| modAL provides uncertainty-based active learning. However, it
| has been shown that uncertainty-based AL fails at batch-wise AL
| for vision datasets and CNNs, see
| https://arxiv.org/abs/1708.00489. Furthermore it only works
| with an initially trained model and thus labeled dataset.
| Lightly offers self-supervised learning to learn high
| dimensional embeddings through its open-source package
| https://github.com/lightly-ai/lightly. They can be used through
| our API to choose a diverse subset. Optionally, this sampling
| can be combined with uncertainty-based AL.
| RicoElectrico wrote:
| Thanks for the reply. In my case I have (hobby) problems that
| fall well into scikit-learn capabilities.
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