15,021 research outputs found
Towards Meta-learning of Deep Architectures for Efficient Domain Adaptation
This paper proposes an efficient domain adaption approach
using deep learning along with transfer and meta-level learning. The objective is to identify how many blocks (i.e. groups of consecutive layers)
of a pre-trained image classification network need to be fine-tuned based
on the characteristics of the new task. In order to investigate it, a number
of experiments have been conducted using different pre-trained networks
and image datasets. The networks were fine-tuned, starting from the
blocks containing the output layers and progressively moving towards
the input layer, on various tasks with characteristics different from the
original task. The amount of fine-tuning of a pre-trained network (i.e.
the number of top layers requiring adaptation) is usually dependent on
the complexity, size, and domain similarity of the original and new tasks.
Considering these characteristics, a question arises of how many blocks
of the network need to be fine-tuned to get maximum possible accuracy?
Which of a number of available pre-trained networks require fine-tuning
of the minimum number of blocks to achieve this accuracy? The experiments, that involve three network architectures each divided into 10
blocks on average and five datasets, empirically confirm the intuition
that there exists a relationship between the similarity of the original
and new tasks and the depth of network needed to fine-tune in order to
achieve accuracy comparable with that of a model trained from scratch.
Further analysis shows that the fine-tuning of the final top blocks of the
network, which represent the high-level features, is sufficient in most of
the cases. Moreover, we have empirically verified that less similar tasks
require fine-tuning of deeper portions of the network, which however is
still better than training a network from scratch
DoShiCo Challenge: Domain Shift in Control Prediction
Training deep neural network policies end-to-end for real-world applications
so far requires big demonstration datasets in the real world or big sets
consisting of a large variety of realistic and closely related 3D CAD models.
These real or virtual data should, moreover, have very similar characteristics
to the conditions expected at test time. These stringent requirements and the
time consuming data collection processes that they entail, are currently the
most important impediment that keeps deep reinforcement learning from being
deployed in real-world applications. Therefore, in this work we advocate an
alternative approach, where instead of avoiding any domain shift by carefully
selecting the training data, the goal is to learn a policy that can cope with
it. To this end, we propose the DoShiCo challenge: to train a model in very
basic synthetic environments, far from realistic, in a way that it can be
applied in more realistic environments as well as take the control decisions on
real-world data. In particular, we focus on the task of collision avoidance for
drones. We created a set of simulated environments that can be used as
benchmark and implemented a baseline method, exploiting depth prediction as an
auxiliary task to help overcome the domain shift. Even though the policy is
trained in very basic environments, it can learn to fly without collisions in a
very different realistic simulated environment. Of course several benchmarks
for reinforcement learning already exist - but they never include a large
domain shift. On the other hand, several benchmarks in computer vision focus on
the domain shift, but they take the form of a static datasets instead of
simulated environments. In this work we claim that it is crucial to take the
two challenges together in one benchmark.Comment: Published at SIMPAR 2018. Please visit the paper webpage for more
information, a movie and code for reproducing results:
https://kkelchte.github.io/doshic
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