PWAI/src/main.tex

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\acmConference{Cross-Model Pseudo-Labeling}{2023}{Linz}

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    \title{Minimize labeling effort of Binary classification Tasks with Active learning}

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    \author{Lukas Heiligenbrunner}
    \email{k12104785@students.jku.at}
    \affiliation{%
        \institution{Johannes Kepler University Linz}
        \city{Linz}
        \state{Upperaustria}
        \country{Austria}
        \postcode{4020}
    }

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    \renewcommand{\shortauthors}{Lukas Heilgenbrunner}

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    \begin{abstract}
        Active learning might result in a faster model convergence and thus less labeled samples would be required. This method might be beneficial in areas where labeling datasets is demanding and reducing computational effort is not the main objective.
    \end{abstract}

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    \keywords{neural networks, ResNET, pseudo-labeling, active-learning}

%\received{20 February 2007}
%\received[revised]{12 March 2009}
%\received[accepted]{5 June 2009}

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    \input{introduction}
    \input{materialandmethods}
    \input{implementation}
    \input{experimentalresults}
    \input{conclusionandoutlook}

    \section{FixMatch}\label{sec:fixmatch}
    There is an already existing approach called FixMatch.
    This was introduced in a Google Research paper from 2020~\cite{fixmatch}.
    The key idea of FixMatch is to leverage the unlabeled data by predicting pseudo-labels out of the known labels.
    Then both, the known labels and the predicted ones are used side by side to train the model.
    The labeled samples guide the learning process and the unlabeled samples gain additional information.

    Not every pseudo prediction is kept to train the model further.
    A confidence threshold is defined to evaluate how `confident` the model is about its prediction.
    The prediction is dropped if the model is too less confident.
    The quantity and quality of the obtained labels is crucial and they have a significant impact on the overall accuracy.
    This means improving the pseudo-label framework as much as possible is essential.

    FixMatch results in some major limitations.
    It relies on a single model for generating pseudo-labels which can introduce errors and uncertainty in the labels.
    Incorrect pseudo-labels may effect the learning process negatively.
    Furthermore, Fixmatch uses a compareably small model for label prediction which has a limited capacity.
    This can negatively affect the learning process as well.
%There is no measure defined how certain the model is about its prediction.
%Such a measure improves overall performance by filtering noisy and unsure predictions.
    Cross-Model Pseudo-Labeling tries to address all of those limitations.

    \subsection{Math of FixMatch}\label{subsec:math-of-fixmatch}
    Equation~\ref{eq:fixmatch} defines the loss-function that trains the model.
    The sum over a batch size $B_u$ takes the average loss of this batch and should be familiar.
    The input data is augmented in two different ways.
    At first there is a weak augmentation $\mathcal{T}_{\text{weak}}(\cdot)$ which only applies basic transformation such as filtering and bluring.
    Moreover, there is the strong augmentation $\mathcal{T}_{\text{strong}}(\cdot)$ which does cropouts and random augmentations.

    \begin{equation}
        \label{eq:fixmatch}
        \mathcal{L}_u = \frac{1}{B_u} \sum_{i=1}^{B_u} {1}(\max(p_i) \geq \tau) \mathcal{H}(\hat{y}_i,F(\mathcal{T}_{\text{strong}}(u_i)))
    \end{equation}

    The indicator function ${1}(\cdot)$ applies a principle called `confidence-based masking`.
    It retains a label only if its largest probability is above a threshold $\tau$.
    Where $p_i \coloneqq F(\mathcal{T}_{\text{weak}}(u_i))$ is a model evaluation with a weakly augmented input.

    \begin{equation}
        \label{eq:crossentropy}
        \mathcal{H}(\hat{y}_i, y_i) = -\sum_{i=1} y_i \cdot log(\hat{y}_i)
    \end{equation}

    The second part $\mathcal{H}(\cdot, \cdot)$ is a standard Cross-entropy loss function which takes two inputs, the predicted and the true label.
    $\hat{y}_i$, the obtained pseudo-label and $F(\mathcal{T}_{\text{strong}}(u_i))$, a model evaluation with strong augmentation.
    The indicator function evaluates in $0$ if the pseudo prediction is not confident and the current loss evaluation will be dropped.
    Otherwise it evaluates to 1 and it will be kept and trains the model further.

    \section{Cross-Model Pseudo-Labeling}\label{sec:cross-model-pseudo-labeling}
    The newly invented approach of this paper is called Cross-Model Pseudo-Labeling (CMPL)\cite{Xu_2022_CVPR}.
    Figure~\ref{fig:cmpl-structure} visualizs the structure of CMPL\@.
    Two different models, a smaller auxiliary model and a larger model are defined.
    They provide pseudo-labels for each other.
    The two different models have a different structural bias which leads to complementary representations.
    This symetric design performs a boost in performance.
    The SG label means 'Stop Gradient'.
    The loss function evaluations are fed into the opposite model as loss.
    The two models train each other.


    \begin{figure}[h]
        \centering
        \includegraphics[width=\linewidth]{../rsc/structure}
        \caption{Architecture of Cross-Model Pseudo-Labeling}
        \label{fig:cmpl-structure}
    \end{figure}

    \subsection{Math of CMPL}\label{subsec:math}
    The loss function of CMPL is similar to that one explaind above.
    But we have to differ from the loss generated from the supervised samples where the labels are known and the unsupervised loss where no labels are available.

    The two equations~\ref{eq:cmpl-losses1} and~\ref{eq:cmpl-losses2} are normal Cross-Entropy loss functions generated with the supervised labels of the two seperate models.


    \begin{align}
        \label{eq:cmpl-losses1}
        \mathcal{L}_s^F &= \frac{1}{B_l} \sum_{i=1}^{B_l} \mathcal{H}(y_i,F(\mathcal{T}^F_{\text{standard}}(v_i)))\\
        \label{eq:cmpl-losses2}
        \mathcal{L}_s^A &= \frac{1}{B_l} \sum_{i=1}^{B_l} \mathcal{H}(y_i,A(\mathcal{T}^F_{\text{standard}}(v_i)))
    \end{align}

    Equation~\ref{eq:cmpl-loss3} and~\ref{eq:cmpl-loss4} are the unsupervised losses.
    They are very similar to FastMatch, but important to note is that the confidence-based masking is applied to the opposite corresponding model.

    \begin{align}
        \label{eq:cmpl-loss3}
        \mathcal{L}_u^F &= \frac{1}{B_u} \sum_{i=1}^{B_u} {1}(\max(p_i^A) \geq \tau) \mathcal{H}(\hat{y}_i^A,F(\mathcal{T}_{\text{strong}}(u_i)))\\
        \label{eq:cmpl-loss4}
        \mathcal{L}_u^A &= \frac{1}{B_u} \sum_{i=1}^{B_u} {1}(\max(p_i^F) \geq \tau) \mathcal{H}(\hat{y}_i^F,A(\mathcal{T}_{\text{strong}}(u_i)))
    \end{align}

    Finally to train the main objective a overall loss is calculated by simply summing all the losses.
    The loss is regulated by an hyperparamter $\lambda$ to enhance the importance of the supervised loss.

    \begin{equation}
        \label{eq:loss-main-obj}
        \mathcal{L} = (\mathcal{L}_s^F + \mathcal{L}_s^A) + \lambda(\mathcal{L}_u^F + \mathcal{L}_u^A)
    \end{equation}

    \section{Architecture}\label{sec:Architecture}
    The used model architectures depend highly on the task to be performed.
    In this case the task is video action recognition.
    A 3D-ResNet50 was chosen for the main model and a smaller 3D-ResNet18 for the auxiliary model.

    \section{Performance}\label{sec:performance}

    In figure~\ref{fig:results} a performance comparison is shown between just using the supervised samples for training against some different pseudo label frameworks.
    One can clearly see that the performance gain with the new CMPL framework is quite significant.
    For evaluation the Kinetics-400 and UCF-101 datasets are used.
    And as a backbone model a 3D-ResNet18 and 3D-ResNet50 are used.
    Even when only 1\% of true labels are known for the UCF-101 dataset 25.1\% of the labels could be predicted right.

    \begin{figure}[h]
        \centering
        \includegraphics[width=\linewidth]{../rsc/results}
        \caption{Performance comparisons between CMPL, FixMatch and supervised learning only}
        \label{fig:results}
    \end{figure}

    \section{Further schemes}\label{sec:further-schemes}
    How the pseudo-labels are generated may impact the overall performance.
    In this paper the pseudo-labels are obtained by the cross-model approach.
    But there might be other strategies as well.
    For example:
    \begin{enumerate*}
        \item Self-First: Each network uses just its own prediction if its confident enough.
        If not, it uses its sibling net prediction.
        \item Opposite-First: Each net prioritizes the prediction of the sibling network.
        \item Maximum: The most confident prediction is leveraged.
        \item Average: The two predictions are averaged before deriving the pseudo-label
    \end{enumerate*}.

    Those are just other approaches one can keep in mind.
    This doesn't mean they are better, in fact they performed even worse in this study.

    \section{Conclusion}\label{sec:conclusion}
    In conclusion, Cross-Model Pseudo-Labeling demonstrates the potential to significantly advance the field of semi-supervised action recognition.
    Cross-Model Pseudo-Labeling outperforms the supervised-only approach over several experiments by a multiple.
    It surpasses most of the other existing pseudo-labeling frameworks.
    Through the integration of main and auxiliary models, consistency regularization, and uncertainty estimation, CMPL offers a powerful framework for leveraging unlabeled data and improving model performance.
    It paves the way for more accurate and efficient action recognition systems.

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