19 research outputs found
Cooperative Lattice Coding and Decoding
A novel lattice coding framework is proposed for outage-limited cooperative
channels. This framework provides practical implementations for the optimal
cooperation protocols proposed by Azarian et al. In particular, for the relay
channel we implement a variant of the dynamic decode and forward protocol,
which uses orthogonal constellations to reduce the channel seen by the
destination to a single-input single-output time-selective one, while
inheriting the same diversity-multiplexing tradeoff. This simplification allows
for building the receiver using traditional belief propagation or tree search
architectures. Our framework also generalizes the coding scheme of Yang and
Belfiore in the context of amplify and forward cooperation. For the cooperative
multiple access channel, a tree coding approach, matched to the optimal linear
cooperation protocol of Azarain et al, is developed. For this scenario, the
MMSE-DFE Fano decoder is shown to enjoy an excellent tradeoff between
performance and complexity. Finally, the utility of the proposed schemes is
established via a comprehensive simulation study.Comment: 25 pages, 8 figure
Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation
We consider the problem of improving the human instance segmentation mask
quality for a given test image using keypoints estimation. We compare two
alternative approaches. The first approach is a test-time adaptation (TTA)
method, where we allow test-time modification of the segmentation network's
weights using a single unlabeled test image. In this approach, we do not assume
test-time access to the labeled source dataset. More specifically, our TTA
method consists of using the keypoints estimates as pseudo labels and
backpropagating them to adjust the backbone weights. The second approach is a
training-time generalization (TTG) method, where we permit offline access to
the labeled source dataset but not the test-time modification of weights.
Furthermore, we do not assume the availability of any images from or knowledge
about the target domain. Our TTG method consists of augmenting the backbone
features with those generated by the keypoints head and feeding the aggregate
vector to the mask head. Through a comprehensive set of ablations, we evaluate
both approaches and identify several factors limiting the TTA gains. In
particular, we show that in the absence of a significant domain shift, TTA may
hurt and TTG show only a small gain in performance, whereas for a large domain
shift, TTA gains are smaller and dependent on the heuristics used, while TTG
gains are larger and robust to architectural choices