37,038 research outputs found
Boosting Self-Supervised Learning via Knowledge Transfer
In self-supervised learning, one trains a model to solve a so-called pretext
task on a dataset without the need for human annotation. The main objective,
however, is to transfer this model to a target domain and task. Currently, the
most effective transfer strategy is fine-tuning, which restricts one to use the
same model or parts thereof for both pretext and target tasks. In this paper,
we present a novel framework for self-supervised learning that overcomes
limitations in designing and comparing different tasks, models, and data
domains. In particular, our framework decouples the structure of the
self-supervised model from the final task-specific fine-tuned model. This
allows us to: 1) quantitatively assess previously incompatible models including
handcrafted features; 2) show that deeper neural network models can learn
better representations from the same pretext task; 3) transfer knowledge
learned with a deep model to a shallower one and thus boost its learning. We
use this framework to design a novel self-supervised task, which achieves
state-of-the-art performance on the common benchmarks in PASCAL VOC 2007,
ILSVRC12 and Places by a significant margin. Our learned features shrink the
mAP gap between models trained via self-supervised learning and supervised
learning from 5.9% to 2.6% in object detection on PASCAL VOC 2007
SUB-Depth: Self-distillation and Uncertainty Boosting Self-supervised Monocular Depth Estimation
We propose SUB-Depth, a universal multi-task training framework for
self-supervised monocular depth estimation (SDE). Depth models trained with
SUB-Depth outperform the same models trained in a standard single-task SDE
framework. By introducing an additional self-distillation task into a standard
SDE training framework, SUB-Depth trains a depth network, not only to predict
the depth map for an image reconstruction task, but also to distill knowledge
from a trained teacher network with unlabelled data. To take advantage of this
multi-task setting, we propose homoscedastic uncertainty formulations for each
task to penalize areas likely to be affected by teacher network noise, or
violate SDE assumptions. We present extensive evaluations on KITTI to
demonstrate the improvements achieved by training a range of existing networks
using the proposed framework, and we achieve state-of-the-art performance on
this task. Additionally, SUB-Depth enables models to estimate uncertainty on
depth output.Comment: bmvc versio
Self-supervised debiasing using low rank regularization
Spurious correlations can cause strong biases in deep neural networks,
impairing generalization ability. While most existing debiasing methods require
full supervision on either spurious attributes or target labels, training a
debiased model from a limited amount of both annotations is still an open
question. To address this issue, we investigate an interesting phenomenon using
the spectral analysis of latent representations: spuriously correlated
attributes make neural networks inductively biased towards encoding lower
effective rank representations. We also show that a rank regularization can
amplify this bias in a way that encourages highly correlated features.
Leveraging these findings, we propose a self-supervised debiasing framework
potentially compatible with unlabeled samples. Specifically, we first pretrain
a biased encoder in a self-supervised manner with the rank regularization,
serving as a semantic bottleneck to enforce the encoder to learn the spuriously
correlated attributes. This biased encoder is then used to discover and
upweight bias-conflicting samples in a downstream task, serving as a boosting
to effectively debias the main model. Remarkably, the proposed debiasing
framework significantly improves the generalization performance of
self-supervised learning baselines and, in some cases, even outperforms
state-of-the-art supervised debiasing approaches
Efficient Version-Space Reduction for Visual Tracking
Discrminative trackers, employ a classification approach to separate the
target from its background. To cope with variations of the target shape and
appearance, the classifier is updated online with different samples of the
target and the background. Sample selection, labeling and updating the
classifier is prone to various sources of errors that drift the tracker. We
introduce the use of an efficient version space shrinking strategy to reduce
the labeling errors and enhance its sampling strategy by measuring the
uncertainty of the tracker about the samples. The proposed tracker, utilize an
ensemble of classifiers that represents different hypotheses about the target,
diversify them using boosting to provide a larger and more consistent coverage
of the version-space and tune the classifiers' weights in voting. The proposed
system adjusts the model update rate by promoting the co-training of the
short-memory ensemble with a long-memory oracle. The proposed tracker
outperformed state-of-the-art trackers on different sequences bearing various
tracking challenges.Comment: CRV'17 Conferenc
- …