5,209 research outputs found
Multi-Domain Adaptation for Image Classification, Depth Estimation, and Semantic Segmentation
The appearance of scenes may change for many reasons, including the viewpoint, the time of day, the weather, and the seasons. Traditionally, deep neural networks are trained and evaluated using images from the same scene and domain to avoid the domain gap. Recent advances in domain adaptation have led to a new type of method that bridges such domain gaps and learns from multiple domains.
This dissertation proposes methods for multi-domain adaptation for various computer vision tasks, including image classification, depth estimation, and semantic segmentation. The first work focuses on semi-supervised domain adaptation. I address this semi-supervised setting and propose to use dynamic feature alignment to address both inter- and intra-domain discrepancy. The second work addresses the task of monocular depth estimation in the multi-domain setting. I propose to address this task with a unified approach that includes adversarial knowledge distillation and uncertainty-guided self-supervised reconstruction. The third work considers the problem of semantic segmentation for aerial imagery with diverse environments and viewing geometries. I present CrossSeg: a novel framework that learns a semantic segmentation network that can generalize well in a cross-scene setting with only a few labeled samples. I believe this line of work can be applicable to many domain adaptation scenarios and aerial applications
Cross-lingual Distillation for Text Classification
Cross-lingual text classification(CLTC) is the task of classifying documents
written in different languages into the same taxonomy of categories. This paper
presents a novel approach to CLTC that builds on model distillation, which
adapts and extends a framework originally proposed for model compression. Using
soft probabilistic predictions for the documents in a label-rich language as
the (induced) supervisory labels in a parallel corpus of documents, we train
classifiers successfully for new languages in which labeled training data are
not available. An adversarial feature adaptation technique is also applied
during the model training to reduce distribution mismatch. We conducted
experiments on two benchmark CLTC datasets, treating English as the source
language and German, French, Japan and Chinese as the unlabeled target
languages. The proposed approach had the advantageous or comparable performance
of the other state-of-art methods.Comment: Accepted at ACL 2017; Code available at
https://github.com/xrc10/cross-distil
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning
In this work, we investigated the teacher-student training paradigm to train
a fully learnable multi-channel acoustic model for far-field automatic speech
recognition (ASR). Using a large offline teacher model trained on beamformed
audio, we trained a simpler multi-channel student acoustic model used in the
speech recognition system. For the student, both multi-channel feature
extraction layers and the higher classification layers were jointly trained
using the logits from the teacher model. In our experiments, compared to a
baseline model trained on about 600 hours of transcribed data, a relative
word-error rate (WER) reduction of about 27.3% was achieved when using an
additional 1800 hours of untranscribed data. We also investigated the benefit
of pre-training the multi-channel front end to output the beamformed log-mel
filter bank energies (LFBE) using L2 loss. We find that pre-training improves
the word error rate by 10.7% when compared to a multi-channel model directly
initialized with a beamformer and mel-filter bank coefficients for the front
end. Finally, combining pre-training and teacher-student training produces a
WER reduction of 31% compared to our baseline.Comment: To appear in ICASSP 202
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