19 research outputs found
Ternary Singular Value Decomposition as a Better Parameterized Form in Linear Mapping
We present a simple yet novel parameterized form of linear mapping to
achieves remarkable network compression performance: a pseudo SVD called
Ternary SVD (TSVD).
Unlike vanilla SVD, TSVD limits the and matrices in SVD to ternary
matrices form in . This means that instead of using the expensive
multiplication instructions, TSVD only requires addition instructions when
computing and .
We provide direct and training transition algorithms for TSVD like Post
Training Quantization and Quantization Aware Training respectively.
Additionally, we analyze the convergence of the direct transition algorithms in
theory.
In experiments, we demonstrate that TSVD can achieve state-of-the-art network
compression performance in various types of networks and tasks, including
current baseline models such as ConvNext, Swim, BERT, and large language model
like OPT
Casting a BAIT for Offline and Online Source-free Domain Adaptation
We address the source-free domain adaptation (SFDA) problem, where only the
source model is available during adaptation to the target domain. We consider
two settings: the offline setting where all target data can be visited multiple
times (epochs) to arrive at a prediction for each target sample, and the online
setting where the target data needs to be directly classified upon arrival.
Inspired by diverse classifier based domain adaptation methods, in this paper
we introduce a second classifier, but with another classifier head fixed. When
adapting to the target domain, the additional classifier initialized from
source classifier is expected to find misclassified features. Next, when
updating the feature extractor, those features will be pushed towards the right
side of the source decision boundary, thus achieving source-free domain
adaptation. Experimental results show that the proposed method achieves
competitive results for offline SFDA on several benchmark datasets compared
with existing DA and SFDA methods, and our method surpasses by a large margin
other SFDA methods under online source-free domain adaptation setting
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations
In real scenarios, state observations that an agent observes may contain
measurement errors or adversarial noises, misleading the agent to take
suboptimal actions or even collapse while training. In this paper, we study the
training robustness of distributional Reinforcement Learning~(RL), a class of
state-of-the-art methods that estimate the whole distribution, as opposed to
only the expectation, of the total return. Firstly, we validate the contraction
of distributional Bellman operators in the State-Noisy Markov Decision
Process~(SN-MDP), a typical tabular case that incorporates both random and
adversarial state observation noises. In the noisy setting with function
approximation, we then analyze the vulnerability of least squared loss in
expectation-based RL with either linear or nonlinear function approximation. By
contrast, we theoretically characterize the bounded gradient norm of
distributional RL loss based on the categorical parameterization equipped with
the Kullback-Leibler~(KL) divergence. The resulting stable gradients while the
optimization in distributional RL accounts for its better training robustness
against state observation noises. Finally, extensive experiments on the suite
of environments verified that distributional RL is less vulnerable against both
random and adversarial noisy state observations compared with its
expectation-based counterpart
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering
Domain adaptation (DA) aims to alleviate the domain shift between source
domain and target domain. Most DA methods require access to the source data,
but often that is not possible (e.g. due to data privacy or intellectual
property). In this paper, we address the challenging source-free domain
adaptation (SFDA) problem, where the source pretrained model is adapted to the
target domain in the absence of source data. Our method is based on the
observation that target data, which might not align with the source domain
classifier, still forms clear clusters. We capture this intrinsic structure by
defining local affinity of the target data, and encourage label consistency
among data with high local affinity. We observe that higher affinity should be
assigned to reciprocal neighbors. To aggregate information with more context,
we consider expanded neighborhoods with small affinity values. Furthermore, we
consider the density around each target sample, which can alleviate the
negative impact of potential outliers. In the experimental results we verify
that the inherent structure of the target features is an important source of
information for domain adaptation. We demonstrate that this local structure can
be efficiently captured by considering the local neighbors, the reciprocal
neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art
performance on several 2D image and 3D point cloud recognition datasets.Comment: Accepted by IEEE TPAMI, extended version of conference paper
arXiv:2110.0420
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification
Lifelong object re-identification incrementally learns from a stream of
re-identification tasks. The objective is to learn a representation that can be
applied to all tasks and that generalizes to previously unseen
re-identification tasks. The main challenge is that at inference time the
representation must generalize to previously unseen identities. To address this
problem, we apply continual meta metric learning to lifelong object
re-identification. To prevent forgetting of previous tasks, we use knowledge
distillation and explore the roles of positive and negative pairs. Based on our
observation that the distillation and metric losses are antagonistic, we
propose to remove positive pairs from distillation to robustify model updates.
Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on
extensive intra-domain experiments on person and vehicle re-identification
datasets, as well as inter-domain experiments on the LReID benchmark. Our
experiments demonstrate that DwoPP significantly outperforms the
state-of-the-art. The code is here: https://github.com/wangkai930418/DwoPP_codeComment: BMVC 202