42 research outputs found
Constrained K-means with General Pairwise and Cardinality Constraints
In this work, we study constrained clustering, where constraints are utilized
to guide the clustering process. In existing works, two categories of
constraints have been widely explored, namely pairwise and cardinality
constraints. Pairwise constraints enforce the cluster labels of two instances
to be the same (must-link constraints) or different (cannot-link constraints).
Cardinality constraints encourage cluster sizes to satisfy a user-specified
distribution. However, most existing constrained clustering models can only
utilize one category of constraints at a time. In this paper, we enforce the
above two categories into a unified clustering model starting with the integer
program formulation of the standard K-means. As these two categories provide
useful information at different levels, utilizing both of them is expected to
allow for better clustering performance. However, the optimization is difficult
due to the binary and quadratic constraints in the proposed unified
formulation. To alleviate this difficulty, we utilize two techniques:
equivalently replacing the binary constraints by the intersection of two
continuous constraints; the other is transforming the quadratic constraints
into bi-linear constraints by introducing extra variables. Then we derive an
equivalent continuous reformulation with simple constraints, which can be
efficiently solved by Alternating Direction Method of Multipliers (ADMM)
algorithm. Extensive experiments on both synthetic and real data demonstrate:
(1) when utilizing a single category of constraint, the proposed model is
superior to or competitive with state-of-the-art constrained clustering models,
and (2) when utilizing both categories of constraints jointly, the proposed
model shows better performance than the case of the single category
When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
Context-based fine-tuning methods, including prompting, in-context learning,
soft prompting (also known as prompt tuning), and prefix-tuning, have gained
popularity due to their ability to often match the performance of full
fine-tuning with a fraction of the parameters. Despite their empirical
successes, there is little theoretical understanding of how these techniques
influence the internal computation of the model and their expressiveness
limitations. We show that despite the continuous embedding space being more
expressive than the discrete token space, soft-prompting and prefix-tuning are
strictly less expressive than full fine-tuning, even with the same number of
learnable parameters. Concretely, context-based fine-tuning cannot change the
relative attention pattern over the content and can only bias the outputs of an
attention layer in a fixed direction. This suggests that while techniques like
prompting, in-context learning, soft prompting, and prefix-tuning can
effectively elicit skills present in the pretrained model, they cannot learn
novel tasks that require new attention patterns
Data Dependent Randomized Smoothing
Randomized smoothing is a recent technique that achieves state-of-art
performance in training certifiably robust deep neural networks. While the
smoothing family of distributions is often connected to the choice of the norm
used for certification, the parameters of these distributions are always set as
global hyper parameters independent of the input data on which a network is
certified. In this work, we revisit Gaussian randomized smoothing and show that
the variance of the Gaussian distribution can be optimized at each input so as
to maximize the certification radius for the construction of the smoothed
classifier. This new approach is generic, parameter-free, and easy to
implement. In fact, we show that our data dependent framework can be seamlessly
incorporated into 3 randomized smoothing approaches, leading to consistent
improved certified accuracy. When this framework is used in the training
routine of these approaches followed by a data dependent certification, we
achieve 9\% and 6\% improvement over the certified accuracy of the strongest
baseline for a radius of 0.5 on CIFAR10 and ImageNet.Comment: First two authors contributed equally to this wor
Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation
Referring Image Segmentation (RIS) - the problem of identifying objects in
images through natural language sentences - is a challenging task currently
mostly solved through supervised learning. However, while collecting referred
annotation masks is a time-consuming process, the few existing
weakly-supervised and zero-shot approaches fall significantly short in
performance compared to fully-supervised learning ones. To bridge the
performance gap without mask annotations, we propose a novel weakly-supervised
framework that tackles RIS by decomposing it into three steps: obtaining
instance masks for the object mentioned in the referencing instruction
(segment), using zero-shot learning to select a potentially correct mask for
the given instruction (select), and bootstrapping a model which allows for
fixing the mistakes of zero-shot selection (correct). In our experiments, using
only the first two steps (zero-shot segment and select) outperforms other
zero-shot baselines by as much as 19%, while our full method improves upon this
much stronger baseline and sets the new state-of-the-art for weakly-supervised
RIS, reducing the gap between the weakly-supervised and fully-supervised
methods in some cases from around 33% to as little as 14%. Code is available at
https://github.com/fgirbal/segment-select-correct