434 research outputs found
Person Recognition in Personal Photo Collections
Recognising persons in everyday photos presents major challenges (occluded
faces, different clothing, locations, etc.) for machine vision. We propose a
convnet based person recognition system on which we provide an in-depth
analysis of informativeness of different body cues, impact of training data,
and the common failure modes of the system. In addition, we discuss the
limitations of existing benchmarks and propose more challenging ones. Our
method is simple and is built on open source and open data, yet it improves the
state of the art results on a large dataset of social media photos (PIPA).Comment: Accepted to ICCV 2015, revise
A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
Training data attribution (TDA) techniques find influential training data for
the model's prediction on the test data of interest. They approximate the
impact of down- or up-weighting a particular training sample. While
conceptually useful, they are hardly applicable to deep models in practice,
particularly because of their sensitivity to different model initialisation. In
this paper, we introduce a Bayesian perspective on the TDA task, where the
learned model is treated as a Bayesian posterior and the TDA estimates as
random variables. From this novel viewpoint, we observe that the influence of
an individual training sample is often overshadowed by the noise stemming from
model initialisation and SGD batch composition. Based on this observation, we
argue that TDA can only be reliably used for explaining deep model predictions
that are consistently influenced by certain training data, independent of other
noise factors. Our experiments demonstrate the rarity of such noise-independent
training-test data pairs but confirm their existence. We recommend that future
researchers and practitioners trust TDA estimates only in such cases. Further,
we find a disagreement between ground truth and estimated TDA distributions and
encourage future work to study this gap. Code is provided at
https://github.com/ElisaNguyen/bayesian-tda
Exploiting saliency for object segmentation from image level labels
There have been remarkable improvements in the semantic labelling task in the
recent years. However, the state of the art methods rely on large-scale
pixel-level annotations. This paper studies the problem of training a
pixel-wise semantic labeller network from image-level annotations of the
present object classes. Recently, it has been shown that high quality seeds
indicating discriminative object regions can be obtained from image-level
labels. Without additional information, obtaining the full extent of the object
is an inherently ill-posed problem due to co-occurrences. We propose using a
saliency model as additional information and hereby exploit prior knowledge on
the object extent and image statistics. We show how to combine both information
sources in order to recover 80% of the fully supervised performance - which is
the new state of the art in weakly supervised training for pixel-wise semantic
labelling. The code is available at https://goo.gl/KygSeb.Comment: CVPR 201
URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates
Representation learning has significantly driven the field to develop
pretrained models that can act as a valuable starting point when transferring
to new datasets. With the rising demand for reliable machine learning and
uncertainty quantification, there is a need for pretrained models that not only
provide embeddings but also transferable uncertainty estimates. To guide the
development of such models, we propose the Uncertainty-aware Representation
Learning (URL) benchmark. Besides the transferability of the representations,
it also measures the zero-shot transferability of the uncertainty estimate
using a novel metric. We apply URL to evaluate eleven uncertainty quantifiers
that are pretrained on ImageNet and transferred to eight downstream datasets.
We find that approaches that focus on the uncertainty of the representation
itself or estimate the prediction risk directly outperform those that are based
on the probabilities of upstream classes. Yet, achieving transferable
uncertainty quantification remains an open challenge. Our findings indicate
that it is not necessarily in conflict with traditional representation learning
goals. Code is provided under https://github.com/mkirchhof/url
Pretrained Visual Uncertainties
Accurate uncertainty estimation is vital to trustworthy machine learning, yet
uncertainties typically have to be learned for each task anew. This work
introduces the first pretrained uncertainty modules for vision models. Similar
to standard pretraining this enables the zero-shot transfer of uncertainties
learned on a large pretraining dataset to specialized downstream datasets. We
enable our large-scale pretraining on ImageNet-21k by solving a gradient
conflict in previous uncertainty modules and accelerating the training by up to
180x. We find that the pretrained uncertainties generalize to unseen datasets.
In scrutinizing the learned uncertainties, we find that they capture aleatoric
uncertainty, disentangled from epistemic components. We demonstrate that this
enables safe retrieval and uncertainty-aware dataset visualization. To
encourage applications to further problems and domains, we release all
pretrained checkpoints and code under https://github.com/mkirchhof/url
Exploring Practitioner Perspectives On Training Data Attribution Explanations
Explainable AI (XAI) aims to provide insight into opaque model reasoning to
humans and as such is an interdisciplinary field by nature. In this paper, we
interviewed 10 practitioners to understand the possible usability of training
data attribution (TDA) explanations and to explore the design space of such an
approach. We confirmed that training data quality is often the most important
factor for high model performance in practice and model developers mainly rely
on their own experience to curate data. End-users expect explanations to
enhance their interaction with the model and do not necessarily prioritise but
are open to training data as a means of explanation. Within our participants,
we found that TDA explanations are not well-known and therefore not used. We
urge the community to focus on the utility of TDA techniques from the
human-machine collaboration perspective and broaden the TDA evaluation to
reflect common use cases in practice.Comment: Accepted to NeurIPS XAI in Action workshop 202
Calibrating Large Language Models Using Their Generations Only
As large language models (LLMs) are increasingly deployed in user-facing
applications, building trust and maintaining safety by accurately quantifying a
model's confidence in its prediction becomes even more important. However,
finding effective ways to calibrate LLMs - especially when the only interface
to the models is their generated text - remains a challenge. We propose APRICOT
(auxiliary prediction of confidence targets): A method to set confidence
targets and train an additional model that predicts an LLM's confidence based
on its textual input and output alone. This approach has several advantages: It
is conceptually simple, does not require access to the target model beyond its
output, does not interfere with the language generation, and has a multitude of
potential usages, for instance by verbalizing the predicted confidence or
adjusting the given answer based on the confidence. We show how our approach
performs competitively in terms of calibration error for white-box and
black-box LLMs on closed-book question-answering to detect incorrect LLM
answers
- …