424 research outputs found
Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks
Deep regression is an important problem with numerous applications. These
range from computer vision tasks such as age estimation from photographs, to
medical tasks such as ejection fraction estimation from echocardiograms for
disease tracking. Semi-supervised approaches for deep regression are notably
under-explored compared to classification and segmentation tasks, however.
Unlike classification tasks, which rely on thresholding functions for
generating class pseudo-labels, regression tasks use real number target
predictions directly as pseudo-labels, making them more sensitive to prediction
quality. In this work, we propose a novel approach to semi-supervised
regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME),
which improves training by generating high-quality pseudo-labels and
uncertainty estimates for heteroscedastic regression. Given that aleatoric
uncertainty is only dependent on input data by definition and should be equal
for the same inputs, we present a novel uncertainty consistency loss for
co-trained models. Our consistency loss significantly improves uncertainty
estimates and allows higher quality pseudo-labels to be assigned greater
importance under heteroscedastic regression. Furthermore, we introduce a novel
variational model ensembling approach to reduce prediction noise and generate
more robust pseudo-labels. We analytically show our method generates higher
quality targets for unlabeled data and further improves training. Experiments
show that our method outperforms state-of-the-art alternatives on different
tasks and can be competitive with supervised methods that use full labels. Our
code is available at https://github.com/xmed-lab/UCVME.Comment: Accepted by AAAI2
Graph Reasoning Transformer for Image Parsing
Capturing the long-range dependencies has empirically proven to be effective
on a wide range of computer vision tasks. The progressive advances on this
topic have been made through the employment of the transformer framework with
the help of the multi-head attention mechanism. However, the attention-based
image patch interaction potentially suffers from problems of redundant
interactions of intra-class patches and unoriented interactions of inter-class
patches. In this paper, we propose a novel Graph Reasoning Transformer (GReaT)
for image parsing to enable image patches to interact following a relation
reasoning pattern. Specifically, the linearly embedded image patches are first
projected into the graph space, where each node represents the implicit visual
center for a cluster of image patches and each edge reflects the relation
weight between two adjacent nodes. After that, global relation reasoning is
performed on this graph accordingly. Finally, all nodes including the relation
information are mapped back into the original space for subsequent processes.
Compared to the conventional transformer, GReaT has higher interaction
efficiency and a more purposeful interaction pattern. Experiments are carried
out on the challenging Cityscapes and ADE20K datasets. Results show that GReaT
achieves consistent performance gains with slight computational overheads on
the state-of-the-art transformer baselines.Comment: Accepted in ACM MM202
Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation
This paper presents a simple yet effective two-stage framework for
semi-supervised medical image segmentation. Our key insight is to explore the
feature representation learning with labeled and unlabeled (i.e., pseudo
labeled) images to enhance the segmentation performance. In the first stage, we
present an aleatoric uncertainty-aware method, namely AUA, to improve the
segmentation performance for generating high-quality pseudo labels. Considering
the inherent ambiguity of medical images, AUA adaptively regularizes the
consistency on images with low ambiguity. To enhance the representation
learning, we propose a stage-adaptive contrastive learning method, including a
boundary-aware contrastive loss to regularize the labeled images in the first
stage and a prototype-aware contrastive loss to optimize both labeled and
pseudo labeled images in the second stage. The boundary-aware contrastive loss
only optimizes pixels around the segmentation boundaries to reduce the
computational cost. The prototype-aware contrastive loss fully leverages both
labeled images and pseudo labeled images by building a centroid for each class
to reduce computational cost for pair-wise comparison. Our method achieves the
best results on two public medical image segmentation benchmarks. Notably, our
method outperforms the prior state-of-the-art by 5.7% on Dice for colon tumor
segmentation relying on just 5% labeled images.Comment: On submission to TM
Oscillation-free Quantization for Low-bit Vision Transformers
Weight oscillation is an undesirable side effect of quantization-aware
training, in which quantized weights frequently jump between two quantized
levels, resulting in training instability and a sub-optimal final model. We
discover that the learnable scaling factor, a widely-used
setting in quantization aggravates weight oscillation. In this study, we
investigate the connection between the learnable scaling factor and quantized
weight oscillation and use ViT as a case driver to illustrate the findings and
remedies. In addition, we also found that the interdependence between quantized
weights in and of a self-attention layer makes
ViT vulnerable to oscillation. We, therefore, propose three techniques
accordingly: statistical weight quantization () to improve
quantization robustness compared to the prevalent learnable-scale-based method;
confidence-guided annealing () that freezes the weights with
and calms the oscillating weights; and
- reparameterization () to resolve the
query-key intertwined oscillation and mitigate the resulting gradient
misestimation. Extensive experiments demonstrate that these proposed techniques
successfully abate weight oscillation and consistently achieve substantial
accuracy improvement on ImageNet. Specifically, our 2-bit DeiT-T/DeiT-S
algorithms outperform the previous state-of-the-art by 9.8% and 7.7%,
respectively. Code and models are available at: https://github.com/nbasyl/OFQ.Comment: Proceedings of the 40 th International Conference on Machine
Learning, Honolulu, Hawaii, USA. PMLR 202, 202
A testability metric for path delay faults and its application
Abstract — In this paper, we propose a new testability metric for path delay faults. The metric is computed efficiently using a non-enumerative algorithm. It has been validated through extensive experiments and the results indicate a strong correlation between the proposed metric and the path delay fault testability of the circuit. We further apply this metric to derive a path delay fault test application scheme for scan-based BIST. The selection of the test scheme is guided by the proposed metric. The experimental results illustrate that the derived test application scheme can achieve a higher path delay fault coverage in scan-based BIST. Because of the effectiveness and efficient computation of this metric, it can be used to derive other design-for-testability techniques for path delay faults. I
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