39 research outputs found
Demystifying Neural Style Transfer
Neural Style Transfer has recently demonstrated very exciting results which
catches eyes in both academia and industry. Despite the amazing results, the
principle of neural style transfer, especially why the Gram matrices could
represent style remains unclear. In this paper, we propose a novel
interpretation of neural style transfer by treating it as a domain adaptation
problem. Specifically, we theoretically show that matching the Gram matrices of
feature maps is equivalent to minimize the Maximum Mean Discrepancy (MMD) with
the second order polynomial kernel. Thus, we argue that the essence of neural
style transfer is to match the feature distributions between the style images
and the generated images. To further support our standpoint, we experiment with
several other distribution alignment methods, and achieve appealing results. We
believe this novel interpretation connects these two important research fields,
and could enlighten future researches.Comment: Accepted by IJCAI 201
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Skeleton based action recognition distinguishes human actions using the
trajectories of skeleton joints, which provide a very good representation for
describing actions. Considering that recurrent neural networks (RNNs) with Long
Short-Term Memory (LSTM) can learn feature representations and model long-term
temporal dependencies automatically, we propose an end-to-end fully connected
deep LSTM network for skeleton based action recognition. Inspired by the
observation that the co-occurrences of the joints intrinsically characterize
human actions, we take the skeleton as the input at each time slot and
introduce a novel regularization scheme to learn the co-occurrence features of
skeleton joints. To train the deep LSTM network effectively, we propose a new
dropout algorithm which simultaneously operates on the gates, cells, and output
responses of the LSTM neurons. Experimental results on three human action
recognition datasets consistently demonstrate the effectiveness of the proposed
model.Comment: AAAI 2016 conferenc
Acute and Subacute Oral Toxicity Evaluation of Eriobotrya japonica
The interest focusing on Eriobotrya japonica leaf triterpene acid (ELTA) has increased recently because of its beneficial effects on health. However, there has been a lack of experimental data on its toxicity. The present study therefore was conducted to evaluate its toxicity in ICR mice. The results showed that ELTA produced neither mortality nor toxicity of the main organs in ICR male and female mice in both acute (0.30, 0.65, 1.39, and 3.00 g·kg−1 body weight) and subacute (150, 300, and 600 mg·kg−1 BW) 28-day toxicity studies. Because of lacking apparently adverse effects found in the hematology, clinical biochemistry, and histopathology evaluation, results of the present study together with the beneficial effects make ELTA as a promising dietary supplement and indicated that ELTA administered orally might have a large safety margin for human
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations
Recommender systems are seen as an effective tool to address information
overload, but it is widely known that the presence of various biases makes
direct training on large-scale observational data result in sub-optimal
prediction performance. In contrast, unbiased ratings obtained from randomized
controlled trials or A/B tests are considered to be the golden standard, but
are costly and small in scale in reality. To exploit both types of data, recent
works proposed to use unbiased ratings to correct the parameters of the
propensity or imputation models trained on the biased dataset. However, the
existing methods fail to obtain accurate predictions in the presence of
unobserved confounding or model misspecification. In this paper, we propose a
theoretically guaranteed model-agnostic balancing approach that can be applied
to any existing debiasing method with the aim of combating unobserved
confounding and model misspecification. The proposed approach makes full use of
unbiased data by alternatively correcting model parameters learned with biased
data, and adaptively learning balance coefficients of biased samples for
further debiasing. Extensive real-world experiments are conducted along with
the deployment of our proposal on four representative debiasing methods to
demonstrate the effectiveness.Comment: Accepted Paper in WWW'2
EGO-TOPO: Environment Affordances from Egocentric Video
First-person video naturally brings the use of a physical environment to the
forefront, since it shows the camera wearer interacting fluidly in a space
based on his intentions. However, current methods largely separate the observed
actions from the persistent space itself. We introduce a model for environment
affordances that is learned directly from egocentric video. The main idea is to
gain a human-centric model of a physical space (such as a kitchen) that
captures (1) the primary spatial zones of interaction and (2) the likely
activities they support. Our approach decomposes a space into a topological map
derived from first-person activity, organizing an ego-video into a series of
visits to the different zones. Further, we show how to link zones across
multiple related environments (e.g., from videos of multiple kitchens) to
obtain a consolidated representation of environment functionality. On
EPIC-Kitchens and EGTEA+, we demonstrate our approach for learning scene
affordances and anticipating future actions in long-form video.Comment: Published in CVPR 2020, project page:
http://vision.cs.utexas.edu/projects/ego-topo