94 research outputs found
On the Learning and Learnablity of Quasimetrics
Our world is full of asymmetries. Gravity and wind can make reaching a place
easier than coming back. Social artifacts such as genealogy charts and citation
graphs are inherently directed. In reinforcement learning and control, optimal
goal-reaching strategies are rarely reversible (symmetrical). Distance
functions supported on these asymmetrical structures are called quasimetrics.
Despite their common appearance, little research has been done on the learning
of quasimetrics.
Our theoretical analysis reveals that a common class of learning algorithms,
including unconstrained multilayer perceptrons (MLPs), provably fails to learn
a quasimetric consistent with training data. In contrast, our proposed Poisson
Quasimetric Embedding (PQE) is the first quasimetric learning formulation that
both is learnable with gradient-based optimization and enjoys strong
performance guarantees. Experiments on random graphs, social graphs, and
offline Q-learning demonstrate its effectiveness over many common baselines.Comment: Project page: https://ssnl.github.io/quasimetric/ Code:
https://github.com/SsnL/poisson_quasimetric_embeddin
Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings
Asymmetrical distance structures (quasimetrics) are ubiquitous in our lives
and are gaining more attention in machine learning applications. Imposing such
quasimetric structures in model representations has been shown to improve many
tasks, including reinforcement learning (RL) and causal relation learning. In
this work, we present four desirable properties in such quasimetric models, and
show how prior works fail at them. We propose Interval Quasimetric Embedding
(IQE), which is designed to satisfy all four criteria. On three quasimetric
learning experiments, IQEs show strong approximation and generalization
abilities, leading to better performance and improved efficiency over prior
methods.
Project Page: https://www.tongzhouwang.info/interval_quasimetric_embedding
Quasimetric Learning Code Package:
https://www.github.com/quasimetric-learning/torch-quasimetricComment: NeurIPS 2022 NeurReps Workshop Proceedings Trac
Image-to-Image Translation with Conditional Adversarial Networks
We investigate conditional adversarial networks as a general-purpose solution
to image-to-image translation problems. These networks not only learn the
mapping from input image to output image, but also learn a loss function to
train this mapping. This makes it possible to apply the same generic approach
to problems that traditionally would require very different loss formulations.
We demonstrate that this approach is effective at synthesizing photos from
label maps, reconstructing objects from edge maps, and colorizing images, among
other tasks. Indeed, since the release of the pix2pix software associated with
this paper, a large number of internet users (many of them artists) have posted
their own experiments with our system, further demonstrating its wide
applicability and ease of adoption without the need for parameter tweaking. As
a community, we no longer hand-engineer our mapping functions, and this work
suggests we can achieve reasonable results without hand-engineering our loss
functions either.Comment: Website: https://phillipi.github.io/pix2pix/, CVPR 201
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
While it is nearly effortless for humans to quickly assess the perceptual
similarity between two images, the underlying processes are thought to be quite
complex. Despite this, the most widely used perceptual metrics today, such as
PSNR and SSIM, are simple, shallow functions, and fail to account for many
nuances of human perception. Recently, the deep learning community has found
that features of the VGG network trained on ImageNet classification has been
remarkably useful as a training loss for image synthesis. But how perceptual
are these so-called "perceptual losses"? What elements are critical for their
success? To answer these questions, we introduce a new dataset of human
perceptual similarity judgments. We systematically evaluate deep features
across different architectures and tasks and compare them with classic metrics.
We find that deep features outperform all previous metrics by large margins on
our dataset. More surprisingly, this result is not restricted to
ImageNet-trained VGG features, but holds across different deep architectures
and levels of supervision (supervised, self-supervised, or even unsupervised).
Our results suggest that perceptual similarity is an emergent property shared
across deep visual representations.Comment: Accepted to CVPR 2018; Code and data available at
https://www.github.com/richzhang/PerceptualSimilarit
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