47 research outputs found
Regularizing Deep Networks by Modeling and Predicting Label Structure
We construct custom regularization functions for use in supervised training
of deep neural networks. Our technique is applicable when the ground-truth
labels themselves exhibit internal structure; we derive a regularizer by
learning an autoencoder over the set of annotations. Training thereby becomes a
two-phase procedure. The first phase models labels with an autoencoder. The
second phase trains the actual network of interest by attaching an auxiliary
branch that must predict output via a hidden layer of the autoencoder. After
training, we discard this auxiliary branch.
We experiment in the context of semantic segmentation, demonstrating this
regularization strategy leads to consistent accuracy boosts over baselines,
both when training from scratch, or in combination with ImageNet pretraining.
Gains are also consistent over different choices of convolutional network
architecture. As our regularizer is discarded after training, our method has
zero cost at test time; the performance improvements are essentially free. We
are simply able to learn better network weights by building an abstract model
of the label space, and then training the network to understand this
abstraction alongside the original task.Comment: to appear at CVPR 201
Visual Object Networks: Image Generation with Disentangled 3D Representation
Recent progress in deep generative models has led to tremendous breakthroughs
in image generation. However, while existing models can synthesize
photorealistic images, they lack an understanding of our underlying 3D world.
We present a new generative model, Visual Object Networks (VON), synthesizing
natural images of objects with a disentangled 3D representation. Inspired by
classic graphics rendering pipelines, we unravel our image formation process
into three conditionally independent factors---shape, viewpoint, and
texture---and present an end-to-end adversarial learning framework that jointly
models 3D shapes and 2D images. Our model first learns to synthesize 3D shapes
that are indistinguishable from real shapes. It then renders the object's 2.5D
sketches (i.e., silhouette and depth map) from its shape under a sampled
viewpoint. Finally, it learns to add realistic texture to these 2.5D sketches
to generate natural images. The VON not only generates images that are more
realistic than state-of-the-art 2D image synthesis methods, but also enables
many 3D operations such as changing the viewpoint of a generated image, editing
of shape and texture, linear interpolation in texture and shape space, and
transferring appearance across different objects and viewpoints.Comment: NeurIPS 2018. Code: https://github.com/junyanz/VON Website:
http://von.csail.mit.edu
Colorization as a Proxy Task for Visual Understanding
We investigate and improve self-supervision as a drop-in replacement for
ImageNet pretraining, focusing on automatic colorization as the proxy task.
Self-supervised training has been shown to be more promising for utilizing
unlabeled data than other, traditional unsupervised learning methods. We build
on this success and evaluate the ability of our self-supervised network in
several contexts. On VOC segmentation and classification tasks, we present
results that are state-of-the-art among methods not using ImageNet labels for
pretraining representations.
Moreover, we present the first in-depth analysis of self-supervision via
colorization, concluding that formulation of the loss, training details and
network architecture play important roles in its effectiveness. This
investigation is further expanded by revisiting the ImageNet pretraining
paradigm, asking questions such as: How much training data is needed? How many
labels are needed? How much do features change when fine-tuned? We relate these
questions back to self-supervision by showing that colorization provides a
similarly powerful supervisory signal as various flavors of ImageNet
pretraining.Comment: CVPR 2017 (Project page:
http://people.cs.uchicago.edu/~larsson/color-proxy/
Learning Shape Priors for Single-View 3D Completion and Reconstruction
The problem of single-view 3D shape completion or reconstruction is
challenging, because among the many possible shapes that explain an
observation, most are implausible and do not correspond to natural objects.
Recent research in the field has tackled this problem by exploiting the
expressiveness of deep convolutional networks. In fact, there is another level
of ambiguity that is often overlooked: among plausible shapes, there are still
multiple shapes that fit the 2D image equally well; i.e., the ground truth
shape is non-deterministic given a single-view input. Existing fully supervised
approaches fail to address this issue, and often produce blurry mean shapes
with smooth surfaces but no fine details.
In this paper, we propose ShapeHD, pushing the limit of single-view shape
completion and reconstruction by integrating deep generative models with
adversarially learned shape priors. The learned priors serve as a regularizer,
penalizing the model only if its output is unrealistic, not if it deviates from
the ground truth. Our design thus overcomes both levels of ambiguity
aforementioned. Experiments demonstrate that ShapeHD outperforms state of the
art by a large margin in both shape completion and shape reconstruction on
multiple real datasets.Comment: ECCV 2018. The first two authors contributed equally to this work.
Project page: http://shapehd.csail.mit.edu
DeepPermNet: Visual Permutation Learning
We present a principled approach to uncover the structure of visual data by
solving a novel deep learning task coined visual permutation learning. The goal
of this task is to find the permutation that recovers the structure of data
from shuffled versions of it. In the case of natural images, this task boils
down to recovering the original image from patches shuffled by an unknown
permutation matrix. Unfortunately, permutation matrices are discrete, thereby
posing difficulties for gradient-based methods. To this end, we resort to a
continuous approximation of these matrices using doubly-stochastic matrices
which we generate from standard CNN predictions using Sinkhorn iterations.
Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet,
an end-to-end CNN model for this task. The utility of DeepPermNet is
demonstrated on two challenging computer vision problems, namely, (i) relative
attributes learning and (ii) self-supervised representation learning. Our
results show state-of-the-art performance on the Public Figures and OSR
benchmarks for (i) and on the classification and segmentation tasks on the
PASCAL VOC dataset for (ii).Comment: Accepted in IEEE International Conference on Computer Vision and
Pattern Recognition CVPR 201
Cross Pixel Optical Flow Similarity for Self-Supervised Learning
We propose a novel method for learning convolutional neural image
representations without manual supervision. We use motion cues in the form of
optical flow, to supervise representations of static images. The obvious
approach of training a network to predict flow from a single image can be
needlessly difficult due to intrinsic ambiguities in this prediction task. We
instead propose a much simpler learning goal: embed pixels such that the
similarity between their embeddings matches that between their optical flow
vectors. At test time, the learned deep network can be used without access to
video or flow information and transferred to tasks such as image
classification, detection, and segmentation. Our method, which significantly
simplifies previous attempts at using motion for self-supervision, achieves
state-of-the-art results in self-supervision using motion cues, competitive
results for self-supervision in general, and is overall state of the art in
self-supervised pretraining for semantic image segmentation, as demonstrated on
standard benchmarks
Neural Expectation Maximization
Many real world tasks such as reasoning and physical interaction require
identification and manipulation of conceptual entities. A first step towards
solving these tasks is the automated discovery of distributed symbol-like
representations. In this paper, we explicitly formalize this problem as
inference in a spatial mixture model where each component is parametrized by a
neural network. Based on the Expectation Maximization framework we then derive
a differentiable clustering method that simultaneously learns how to group and
represent individual entities. We evaluate our method on the (sequential)
perceptual grouping task and find that it is able to accurately recover the
constituent objects. We demonstrate that the learned representations are useful
for next-step prediction.Comment: Accepted to NIPS 201
Ambient Sound Provides Supervision for Visual Learning
The sound of crashing waves, the roar of fast-moving cars -- sound conveys
important information about the objects in our surroundings. In this work, we
show that ambient sounds can be used as a supervisory signal for learning
visual models. To demonstrate this, we train a convolutional neural network to
predict a statistical summary of the sound associated with a video frame. We
show that, through this process, the network learns a representation that
conveys information about objects and scenes. We evaluate this representation
on several recognition tasks, finding that its performance is comparable to
that of other state-of-the-art unsupervised learning methods. Finally, we show
through visualizations that the network learns units that are selective to
objects that are often associated with characteristic sounds.Comment: ECCV 201