fabiochiu/medium-articles huggingface dataset:
Gaslighting, Negging, Lampshading, Oh My!
By: Robin Oakman
Tags: Manipulation, Power, Gaslighting, Negging, Psychology

Gaslighting is the technique of convincing someone to fear something that is unknown and seems untrue. However, subjected repeatedly to the possibility of the gaslighter’s version of reality, the victim, without even realizing it, begins to internalize this new reality as their own. Lampshading encourages victims to twist the completely unbelievable into another realm of distortion. Realms of distortion that can become cults.

The reward for the “actor” is gaining power over their victim.

Gaslighting is emotional/verbal abuse that can be described as covert emotional and psychological manipulation. Manipulation is a learned skill, very likely in response to power imbalances experienced as a child.

The child learns to manipulate as a defense mechanism and is more likely to continue into adulthood as abuse toward people they perceive as upsetting the balance of power in their adult life.

Stephanie A. Sarkis, Ph.D., in a Psychology Today blog post from January 2017, provides a list of 11 warning signs of gaslighting.

1. They tell blatant lies. 2. They deny they ever said something, even though you have proof. 3. They use what is near and dear to you as ammunition. 4. They wear you down over time. 5. Their actions do not match their words. 6. They throw in positive reinforcement to confuse you. 7. They know confusion weakens people. 8. They project. 9. They try to align people against you. 10. They tell you or others that you are crazy. 11. They tell you everyone else is a liar.

The manipulative behavior may be reflexive (on auto-pilot) but is ALWAYS conscious and deliberate. There are no exceptions; there are no accidents.

This is certainly true in my mother’s case. She suffered from mental illness (I suspect bipolar disorder), was herself committed to a psychiatric hospital on two occasions, received electroconvulsive therapy (in the early 1960s), but never received followup treatment or acknowledged to herself or anyone else that she had (or had ever had) a problem.
Projeto CEO: O que é? Como surgiu? Qual o objetivo?
By: Fernanda Formicola
Tags: Power, Empreendedorismo, Girls, Women, CEO

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
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Tags: Fame, Money, Protectio N, Connection, Powers

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“The Power of Music”
By: Jenson Alves
Tags: Power, Kresge College, Music, Representation

Meet Google Drive - One place for all your files

Google Drive is a free way to keep your files backed up and easy to reach from any phone, tablet, or computer. Start…
The Ice King: 2 The Kiss
By: Eden Frei
Tags: Sensuality, Power, Attraction, Fantasy, Poetic Prose

The Ice King doesn’t speak, and I don’t ask; the questions are too many, too small: too trivial by comparison.

I feel my body tremble, not with fear, not with cold; with unfamiliarity? I look him in the eyes, and their glint reassures me: I want his power to be absolute, but benign.

As I take off my heavy boots and both pairs of socks, I expect the ice under my feet to sting or to burn me, but with my eyes on him still, and his gaze still steady on me, there is only the glow that expands from inside my spine.

I take a step towards him, and his presence feels no longer silent, it hums, or so my mind makes me believe; in truth he lies still and alert, and my breathing is no longer shallow: I want to melt into him, meld with him, and as I step closer, he sits up just enough to extend his hand and bring me into his orbit.

Now the colours, the touch, the sensations, the heat from within, the cold from without; this surface I lie on is as hard as polished marble, this skin that I breathe is softer than ermine, but his grip and his hold and his motion are firm; no longer can I tell what am I and what he, my focus is gone, the ice and The Ice King, the light and the scent are all one; I dissolve into it, into him, into the fire of him in me; into the ice that is no longer chill but a mould of clean edges that envelop us like multiple layers of soothing gauze, like everything ever imagined but more, and more real, like losing myself, my thought and my fear, like everything ever felt but not known, like owning the universe through being owned, desiring only being desired, like being The Ice King through being his, not wanting not pining not longing not hoping not dreading not doing not acting not willing not giving not taking not talking not buying not selling not looking forward and not thinking back, not imagining and not dreaming. Being and ceasing to be all at once in the now and forever.

The Now. The Forever. We breathe. We hold on to each other. I think I smile, but I can’t be sure. He tilts his head back and exhales. I feel his breath on my neck and bury my face in his shoulder. The light is orange and blue and a bit purple too, and we are embedded in the ice that feels now as if it had melted and made a pool of clear water that seems to flow warm, although this may just be the pulse in my temple and the beat of his heart and the protective embrace of his arm and the comfort, the comfort of him.

We lie thus for hours, or so it seems, as I drift in and out of awareness, and The Ice King is deep in my mind, quiet and quite majestic. I know I can’t stay here but nor can I leave. I bathe in the silence, but words are bubbling inside me. I want for nothing now, but I wonder how deep, how old, how immaterial is the ice. I lift my head to look at his face, in repose. His lips are not of this world. I hesitate. I pause. I cannot ask permission. I cannot resist. I kiss him.
hey baye bes(call it amazing)
By: Moses In The Wild
Tags: Engagement Ring, Power, Love Letters, Love And Sex

I don’t wonder about why I do anything anymore. A few minutes of quality after care promotes the most delicately brutal of storms.. we brought home a fox.. we confessed .. and you used my giant chair to flood..

Today I know no lie..

You must never stop meeting me this way. You must never cease biting your wrist. I am not the trap and your legs weak and shaking I am no trap..

Have you ever been so free?

It is with great discernment that I watch you.. I like the sight of the disappearance, reappearance… so dark against your open sky.. a storm cloud moving beside you… you walking that fox .. walking it by my surrender.. no mames.. zero …

So I accept it .. no matter what… all of it… no matter what.. walking inside the outside of your landscape … you whispered it’s name “free land”.

The diamonds are brown and when cut with the hunters flint they are more brilliant — — — — Moses in the Wild

art credit : Matador, photo by Peter Müller.
Urge For Motivation
Tags: Motivation, Instagram, Power, Hustle, India

Everyone needs some inspiration, and these motivational quotes will give you the edge you need to create your success. So read on and let them inspire you. As entrepreneurs, leaders, managers, and bosses, we must realize that everything we think actually matters. If we are seeking success, we must think successful, inspiring, and motivating thoughts.

Urge For Motivation is a place where you get daily motivation, power, inspiration, vision. A community full of hustlers who are working hard to achieve there goals and surely want to help others hustling to be more than a average one.

If you find yourself as a hustler than please spare a minute or two and check out our page: https://www.instagram.com/urgeformotivation/

Thank you for staying till the end. Please follow us here for daily new facts and articles.
Affirmation Monday (07122020)
By: Blogfessions Of A Nomadic Spirit
Tags: Affirmation, Butterfly Effect, Power, Existence, Interconnectedness

Whenever I was alone, I would be thinking about what it would be like if I were never born. When I did this, I never thought about the contribution I made but dwelled on my failures. As a result, I concluded that I was insignificant. I kept this belief until I truly started to love myself. I realised that my power was tied to my self worth; so once I started changing the narrative, the outcome was better.

I realised that my presence affected so many people; I listened, always tried to make others smile and offered assistance (where possible). I had to remember that we, as humans, are interconnected and the simplest things we do affect others. It’s so easy to get carried away into believing that we are solo travellers due to the social media craze. However, this aspect should be taken into consideration much more as our reach is wider and sometimes instantaneous.

I’m now mindful of all the energy I put out into the world for it is important that others feel worthy. The way you act towards someone can change the course of the world. I am using my influence to uplift others as I believe that the level of abundance you acquire is based on how much you help others.

Best Wishes and Be Safe!

Happy Manifesting,

Nomadic (Free) Spirit
8. The Dolt of Volt
By: Greg Gradwell
Tags: Elixir, Autopilot, Harry Potter, Power, Sensors

I don’t know what to tell you, this one straight up refuses to be embedded from Github. You just gotta go straight to the source.

https://gist.github.com/greghgradwell/16b4a6bf2f436c3f33ed4bd4bfb0c3a3
Books in Black
By: Zainab Jafri
Tags: Power, Satire, Censorship, Books, Poetry

Books in Black

Photo by Adam Birkett on Unsplash

Mr. Shopkeeper,

Assalamualaekum!

I’m in a rush

Do you have these books

By such and such?

I’m sorry Mr. in-rush

It’s black in law

Black for what?

Mr. Mad becomes

Of Mr. in-rush

Don’t know as much

Couldn't care less

How about this one?

Not available yet

Yet or forever?

I am not in power,

Whatsoever!

Mr. Mad gets madder

Ugh, ugh, ugh!

How can wood shreds

Hurt so much?

I’ve got the money,

I’ve got my clutch

Buy the recommended

It’s clean, no rut

You’ve got the money

You’ve got the clutch

Not my taste

Thank you very much
It’s about power, stupid.
By: Ndsu Pcpe
Tags: Blog, Politics, Founding Fathers, Power, Government

By Zachery Tiedeman

Listen the next time you hear a political debate. Pay attention to the partisanship in Washington. If you listen carefully, you will find that all of our “hot button topics” have been subverted to serve a specific political battle. A battle over power.

This battle was initiated by our founding fathers, and today we see it becoming increasingly polarized, threatening to tear our nation apart. What is the battle? It boils down to a turf war between our states and the federal government. Who should make decisions? Where should power be consolidated? Should government be big or small? These questions were on the minds of the founding fathers, and as some of our nation’s greatest thinkers, they established a system to keep these questions in perpetual struggle.

They believed those with power would always try to gain more. They feared a state or federal government too powerful would begin to act in self-interest, subjugating the people and hurting the nation. A battle between the two, our founding fathers thought, would result in a balance. Neither side would gain full power, and a happy medium would be reached.

This struggle was front and center at a recent Constitution Day event I attended. NDSU’s Center for Public Choice and Private Enterprise hosted a “Recreating the Constitution” simulation designed to emulate the debates surrounding key issues at the Constitutional Convention. This event provided a platform for me to understand the political process without being clouded by the emotions of our present-day environment.

An example from the event: a delegate supporting states’ rights insisted only “educated people” should serve as representatives, and thus we should have very few delegates. The delegate argued that the best system would have the same number of representatives for every state. Although the actual wording made it sound quite reasonable, it was really an emotional appeal to a hot button topic of the time. The goal was to justify more power for the smallest states in the union.

Looking at modern politics, we see similar conflations happening over and over. People argue, “The states should be able to decide Y.” Or, from the opposite side of the aisle, “We should all participate in X.” Any argument containing these basic ideas is more focused on the issue of power than the topic at hand. In fact, it is almost certainly an emotional appeal to justify the consolidation or distribution of power.

I look at political discourse today and think that at some point we forgot the original struggle of American politics. We forgot that people in power — democrat or republican, president or senator — will continue to seek one thing: more power. We forgot the central balance of our political system: to protect the people and not the politicians.

So next time a politician talks and you feel yourself getting fired up:

Take a deep breath and remember: If you ignore their fear mongering, YOU win.
What does freedom of choice mean to you?
By: Lauren Jane Heller
Tags: Power, Mindset, Freedom, Presence, Mindfulness

Photo by Annabelle Agnew

My deepest fear is of being trapped. I am more afraid of being stuck in an existence I don’t love, that doesn’t stimulate and excite me, than I am of the great unknown, of risk or possible challenges beyond the horizon.

For me, the idea of settling feels like a slow death. The thought that there isn’t something shinier and more exciting to do, new opportunities, new knowledge to gain, seems like it’s all worthless. What’s it all for if there’s no new experience?

And yet…I recognize that when I get into that mode of seeking and hungering and hankering after the next new thing, I lose the freedom I so deeply desire. The need to keep searching is itself a cage — it’s a trap to always want more, to never feel satisfied, to never feel accomplished.

I spent much of my young life searching for the next thrill, the new feeling, the shiny thing. Impulsive choices, world travel, questionable partners… I leapt from excitement to excitement, challenging myself to be better and always choosing more, seeking newness with every leap.

Yet, despite all of the living I packed in, I still didn’t feel satisfied. I didn’t feel accomplished or worthy of the story of myself I was chasing. I still had my eyes on more.

There are many parables and stories about people who just want a little more. I vividly remember, during my first Vipassana meditation retreat, hearing one such story. There is a man who advances from a one-room shanty to a fleet of Rolls Royces and still that hunger, that need for something else, can’t be sated. He wants everything and in that desire, despite all he has accomplished, he feels he has nothing.

It took a while for me to embody that teaching — to feel it deep inside me, to shift into the abundance that simply is but you can only notice when you stop craving.

And so now, when I find myself thinking “what’s next?”, I remind myself that being right here, right now is true freedom.

That excitement I’m looking for? It’s already here if I stop looking to the future.

Freedom is knowing that now is perfect as it is and that there are no “shoulds” — there is no other way that this or I ought to be.

And so, I take a deep breath and I sit and revel in that knowledge. I feel it in my body — in every cell. This moment is perfect. As long as I take care of now, as long as I bring myself back here to feel its expansive, pulsing, brilliant aliveness, then I am free.
The Power of Color
Tags: Power, Colors, Yellow

Photo by Paweł Czerwiński on Unsplash

Early humans watched their fires blacken the ceilings of the caves where they lived. They saw blue and red in the sky and brown and green on the ground. In time, people began to understand that color not only made the world more beautiful, it was also able to convey emotion and symbolize power. Using colors they extracted from insects, plants, and minerals, primitive humans copied animals, painting their bodies to signal aggression toward any enemy or to attract a mate.

Over the centuries, the sources of colors such as blue, purple, and red were carefully guarded and were often worth as much as gold. In the 19th century, a young chemistry student became the first to manufacture a synthetic dye, and suddenly the world became a much colorful place. By the 20th century, as scientists discovered the psychological effects of colors, everyone from advertisers to educators found ways to make use of color to influence our feelings and behavior.

Red

Photo by James Lee on Unsplash

Red, the color of human blood, has traditionally symbolized intensity, fire, love, and anger. In eastern cultures, it also represents luck, wealth, and success. In humans, the color red can send messages. Some people redden, for example, when they are angry or embarrassed, sending a clear signal to others about how they are feeling. British anthropologist Russell Hill and Robert Barton of the University of Durham found that when opponents in a game are equally matched, the team dressed in red is more likely to win. Why? According to Barton, ‘red seems to be the color, across species, that signal dominance—giving those dressed in red an advantage in sporting events. In many animal species, contact with this bold color causes the heart rate to accelerate. However, one of red’s lighter shape, pink, can have the opposite effect on people. Men in prisons are reported to be more passive when the walls are painted a specific shape of pink.

Yellow

Photo by James Lee on Unsplash

Yellow, the color we most often associate when sunshine, is found throughout nature and the man-made world as a color that commands attention; indeed, it is one of the easiest colors to see. This highly visible shape to traffic signs and pens used to highlight important information in a text. The color is also used to caution people. Soccer players, for example, are shown yellow as a reminder to behave. It can be used as a stimulant as well: In a number of students, yellow has been found to help children focus on their work and do better in school.
Review — Group Norm (GN): Group Normalization (Image Classification)
By: Sik-Ho Tsang
Tags: Image Classification, Artificial Intelligence, Convolutional Network, Batch Normalization, Deep Learning

Review — Group Norm (GN): Group Normalization (Image Classification)

Error Rate by Batch Norm (BN) Increases When Batch Size Decreasing While Group Norm (GN) Still Can Maintain Similar Error Rate

In this story, Group Normalization, Group Norm (GN), by Facebook AI Research (FAIR), is presented.

In conventional Batch Norm (BN):

Normalizing along the batch dimension introduces problems — BN error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation.

This limits BN’s usage for training larger models, like computer vision tasks including detection, segmentation, and video.

In this paper:

Group Normalization (GN) is treated as a simple alternative to BN .

. GN divides the channels into groups and computes within each group the mean and variance for normalization.

GN’s computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes.

This is a paper in 2018 ECCV with over 900 citations. (Sik-Ho Tsang @ Medium)
[Review] Purohit ICIP’18: Learning Based Single Image Blur Detection (Blur Detection)
By: Sik-Ho Tsang
Tags: Blur Detection, Defocus Map Estimation, Artificial Intelligence, Convolutional Network, Deep Learning

[Review] Purohit ICIP’18: Learning Based Single Image Blur Detection (Blur Detection)

Blur Detection Results

In this story, Learning Based Single Image Blur Detection and Segmentation, Purohit ICIP’18, by Indian Institute of Technology Madras, is reviewed. In this paper:

Global context and local features are jointly learned in the network. Two sub-networks are trained to perform the task at global (image) and local (patch) levels.

are trained to perform the task at The pixel-level probabilities are aggregated and estimated by two networks and then fed to a MRF based framework which returns a refined and dense segmentation-map.

This is a paper in 2018 ICIP. (Sik-Ho Tsang @ Medium)
Review — BDNet: Blur Detection Convolutional Neural Network (Blur Detection)
By: Sik-Ho Tsang
Tags: Blur Detection, Defocus Map Estimation, Artificial Intelligence, Convolutional Network, Deep Learning

Review — BDNet: Blur Detection Convolutional Neural Network (Blur Detection)

BDNet Variants

In this story, Multiscale blur detection by learning discriminative deep features, BDNet, by Tianjin University, and Civil Aviation University of China, is reviewed. In this paper:

A simple yet effective 6-layer CNN model , with 5 layers for feature extraction and 1 for binary classification is proposed, which can faithfully produce patch-level blur likelihood .

, with 5 layers for feature extraction and 1 for binary classification is proposed, which can faithfully . The network is applied at three coarse-to-fine scales. The multiscale blur likelihood maps optimally fused to generate better blur detection.

This is a paper in 2018 JNEUCOM with over 20 citations where JNEUCOM is a journal in Elsevier with a high impact factor of 4.438. (Sik-Ho Tsang @ Medium)
[Review] Pix2Pix: Image-to-Image Translation with Conditional Adversarial Networks (GAN)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Convolutional Network, Gan, Generative Adversarial, Deep Learning

In this story, Image-to-Image Translation with Conditional Adversarial Networks, Pix2Pix, by Berkeley AI Research (BAIR) Laboratory, UC Berkeley, is presented. In this paper:

Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems.

are investigated as These networks not only learn the mapping from input image to output image , but also learn a loss function to train this mapping.

, but also This approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.

This is a paper in 2017 CVPR with over 7000 citations. (Sik-Ho Tsang @ Medium)

There is a funny online demo for Pix2Pix: https://affinelayer.com/pixsrv/
[Review] BTBCRL (BTBNet + CRLNet): Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Network (Blur Detection)
By: Sik-Ho Tsang
Tags: Blur Detection, Defocus Map Estimation, Artificial Intelligence, Convolutional Network, Deep Learning

[Review] BTBCRL (BTBNet + CRLNet): Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Network (Blur Detection)

New defocus blur detection (DBD) dataset of 1100 Challenging Images

In this story, Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Network, BTBCRL (BTBNet + CRLNet), by Dalian University of Technology, and Chinese Academy of Sciences, is presented.

This paper is the extension of BTBNet in 2018 CVPR. Thus, mainly the major differences will be mentioned in the story.

Instead of using FRRNet, a cascaded DBD map residual learning network (CRLNet) is proposed to gradually restore finer structures from the small scale to the large scale , which can accurately distinguish homogeneous regions and suppress the background clutter .

is proposed to , which can and . With BTBNet plus the new CRLNet, it forms the network called BTBCRL.

This is a paper in 2020 TPAMI where TPAMI has high impact factor of 17.861. (Sik-Ho Tsang @ Medium)
Building a CNN to detect brain tumors
By: Isaac Kennedy Diniz Gomes
Tags: Brain Tumor, Convolutional Network, Data Science

The prepreprocessing of the dataset begins by augmenting our data. This technique is highly used to reduce overfitting by adding slightly modified copies of our original data.

Code snippet to augment the data.

Also, to increase the model efficiency, OpenCV was used to crop the images so we reduce the amount of useless, blank (or sometimes black) space on the images, as we can see in the following picture.

Function used to crop the images

Image before (left) and after (right) the cropping process.

After the preprocessing, I’ve plotted a few examples of the current dataset to show the results of the cropping/augmentation steps.

A few examples of healthy brains present on the dataset.
[Review] BTBNet: Multi-Stream Bottom-Top-Bottom Fully Convolutional Network (Defocus Blur Detection)
By: Sik-Ho Tsang
Tags: Blur Detection, Defocus Map Estimation, Artificial Intelligence, Convolutional Network, Deep Learning

[Review] BTBNet: Multi-Stream Bottom-Top-Bottom Fully Convolutional Network (Defocus Blur Detection)

A challenging example for defocus blur detection

In this story, Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network, BTBNet, by Dalian University of Technology, and Chinese Academy of Sciences, is presented. In this story:

A multi-stream bottom-top-bottom fully convolutional network (BTBNet) is proposed, which is the first attempt of an end-to-end deep network . Low-level cues are integrated with high-level semantic information.

is proposed, which is the . Low-level cues are integrated with high-level semantic information. Multi-stream BTBNets are used to handle input images with different scales.

are used to handle input images with different scales. A fusion and recurrent reconstruction network is also designed to recurrently refine the preceding blur detection maps.

is also designed to recurrently refine the preceding blur detection maps. A new challenging dataset is also constructed.

This is a paper in 2018 CVPR with over 30 citations. (Sik-Ho Tsang @ Medium)
[Review] Evaluating Weakly Supervised Object Localization Methods Right (Weakly Supervised Object Localization)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Object Localization, Convolutional Network, Deep Learning, Weakly Supervised

[Review] Evaluating Weakly Supervised Object Localization Methods Right (Weakly Supervised Object Localization)

Accuracy Measured Using New Protocol

In this story, Evaluating Weakly Supervised Object Localization Methods Right, by Yonsei University, LINE Plus Corp., NAVER Corp., and University of Tuebingen, is presented. In this paper:

A new evaluation protocol is proposed where full supervision is limited to only a small held-out set not overlapping with the test set.

where full supervision is limited to only a small held-out set not overlapping with the test set. Five most recent WSOL methods have not made a major improvement over the CAM baseline as shown above.

as shown above. Existing WSOL methods have not reached the few-shot learning baseline, where the full-supervision at validation time is used for model training.

This is a paper in 2020 CVPR with over 24 citations. (Sik-Ho Tsang @ Medium)
Makine Öğrenmesi | Ördü Kader Ağlarını Bölüm 2— Evrişimli Sinir Ağları
By: Samed Harman
Tags: Yapay Zeka, Convolutional Network, Computer Science, Machine Learning, Deep Learning

Samed Harman - Necmettin Erbakan University - Kahramankazan, Ankara, Turkey | LinkedIn

View Samed Harman's profile on LinkedIn, the world's largest professional community. Samed has 1 job listed on their…
GENERATE FACES WITH SPECIFIC ATTRIBUTES
By: Alex Rossi
Tags: Generative Adversarial, Generation, Convolutional Network, Deep Learning

GAN

The developed GAN has been trained on 192 599 images and its assessment through the FID score has been conducted on the remaining 10 000 images. In particular, the discriminator has been trained separately on fake and real images and with batches of size 64.

Model’s architecture

Following the tricks to improve the stability of GANs by S.Chintala [2], the hereunder best practices has been introduced in the model:

The input data has been normalized to the range [-1, 1] before the training

The output layer of the generator has the hyperbolic tangent as activation function

For the remaining layers, in both discriminator and generator, the leaky version of a Rectified Linear Unit was used as activation function with a negative slope coefficient (alpha) of 0.2.

A spherical latent space has been used, with 100 random variables identically distributed according to a standard normal distribution.

A dropout layer has been added to the discriminator with a rate parameter of 0.4

The model was trained for 50 epochs with a resulting FID score of 23.

Sample of randomly generated faces
Introduction to Object Centric Learning
By: Michele De Vita
Tags: Convolutional Network, Machine Learning, Unsupervised Learning, Object Detection, Deep Learning

Metrics

An hard and intriguing part is the choose of the metric i.e. the function that establish the goodness of our Object Centric Learner (from now on OCL). I’ve said hard because compared to other model categories like image classifier or generators (where you can use accuracy, f1 score or log likelihood) the metric of a OCL has to be permutation invariant because the order in which our Object Centric Learner finds the object could not be the same of the dataset but any order is fine as long the OCL finds the Objects.

For example if our model finds in this order {cube, sphere, background} the metric should be the same if the model finds in this order {background, cube, sphere} even if in the dataset the order is {sphere, background, cube}.

ARI

A popular permuation invariant metric used in OCL models is ARI (Adjusted Rand Index) which is originally introduced for Clustering comparison but now also used to compare the Object true masks with the Object predicted masks from the model.

The trick to adapt this Clustering metric to OCL is to consider each Object as a Cluster and every pixel as an data observation: basically each pixel is assigned to an Object (i.e. a cluster) using the object mask in which has the highest value (for example if pixel [3, 10] has value 0.1 for object 1, 0.5 for object 2 and 0.4 for object 3 therefore this pixel is assigned to “cluster 2”)

An Example of clustering based visualization of the Image. Above we have the input image, while below we have assigned a different color to every pixel based on cluster inclusion.

Model Architectures

At the moment of this article there isn’t a predominant model architecture but many different that works well, for example we have:

Slot Attention introduce an Attention Mechanism along with a recurrent process to accurately estimate the latent space of each object followed by a standard decoder to get the mask and the object image

Slot Attention Architecture

MONet uses an UNet to get the masks for each object followed by a Variational AutoEncoder to get the objects reconstructed images and object latent space

MONet architecture

GENESIS has a Variational AutoEncoder to estimate the masks of each object followed by another VAE that receive in input the image and the component mask to get the object image and the latent space

GENESIS Architecture

SPACE models the foreground with bounding boxes while the background is modeled as a set of K objects

SPACE architecture

References
Segmentation Based Interpretability of CNN Classification
By: Arnab Das
Tags: Model Interpretability, Convolutional Network, Machine Learning, Data Science, Deep Learning

High-Level Segmentation Based Interpretability:

Human vision is different from computer vision in two main aspects. Firstly, the human brain is a huge source of prior knowledge, acquired by diverse sensory organs, experience, and memory. The deep learning model lacks this sort of prior knowledge for the vision-related task. And secondly, when we see a picture, rather than focussing on the complete image we focus (pay attention to) on different areas of the image, gather high-level features, and then consolidate all that high-level feature to decide on the image. So, if we ask ourselves why the input is an image of digit 7. We probably answer in a fashion that it has got a horizontal line along with a connected slanting vertical line and it matches our previous knowledge of digit 7, hence this input image is actually of class 7.

Can we get this level of interpretation from the CNN model? To find this out, I have employed a special technique. I have segmented the input image with the ‘Felzenszwalb’ method using the ‘skimage’ library and rather than the whole image giving as input to the model, I have given individual segments as input to the model and predicted the class along with the score.

Fig 6: Different segments of the actual input and respective predictions from the model | Image by author

I find the outcome of this experiment unusual, interesting, uncanny, and dangerous at the same time. If you have a look at the top three segments, which are nothing but the horizontal line from the actual image of digit 7, The model can predict those as class 7 with an almost near-perfect score. Those segments are nothing like digit 7. Whereas the 4th segment which somewhat like digit 7 the prediction score comes down to 0.913.

This finding further underscores the question, what the network is actually learning. Is it at all able to learn any high-level features like we human do or it just finds some low-level interaction of different intensity patterns of the pixels and classifies the images based on the presence or absence of those patterns?
[Review] ABN: Attention Branch Network for Visual Explanation (Image Classification)
By: Sik-Ho Tsang
Tags: Image Classification, Artificial Intelligence, Convolutional Network, Attention Network, Deep Learning

[Review] ABN: Attention Branch Network for Visual Explanation (Image Classification)

Attention Branch Network (ABN)

In this story, Attention Branch Network: Learning of Attention Mechanism for Visual Explanation, ABN, by Chubu University, is presented. In this paper:

Attention Branch Network (ABN) is proposed, which extends a response-based visual explanation model by introducing a branch structure with an attention mechanism.

is proposed, which extends a by introducing a branch structure with an attention mechanism. ABN can be applicable to several image recognition tasks by introducing a branch for the attention mechanism and is trainable for visual explanation and image recognition in an end-to-end manner.

This is a paper in 2019 CVPR with over 60 citations. (Sik-Ho Tsang @ Medium)
[Review] Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization (Weakly Supervised Object Localization)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Object Localization, Convolutional Network, Deep Learning, Weakly Supervised

[Review] Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization (Weakly Supervised Object Localization)

Grad-CAM ‘Cat’

In this story, Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, Grad-CAM, by Georgia Institute of Technology, and Facebook AI Research, is shortly presented. In this paper:

Grad-CAM is applicable to a wide variety of CNN model-families .

. It creates a class-discriminative visualization .

. It also lend insights into failure modes of the model.

Due to the above advantages, indeed it is not just used for WSOL, but also other tasks, which makes this paper gaining high citations. This is a paper in 2017 ICCV with over 3200 citations. (Sik-Ho Tsang @ Medium)
[Review] ADL: Attention-based Dropout Layer (Weakly Supervised Object Localization)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Object Localization, Convolutional Network, Deep Learning, Weakly Supervised

[Review] ADL: Attention-based Dropout Layer (Weakly Supervised Object Localization)

Weakly Supervised Object Localization (WSOL)

In this story, Attention-based Dropout Layer forWeakly Supervised Object Localization, ADL, by Yonsei University, is presented.

Weakly Supervised Object Localization (WSOL) is to have object localization while without object bounding box labels, but with only image-level label, for training. In this paper:

Attention-based Dropout Layer (ADL) is used to hide the most discriminative part from the model for capturing the integral extent of object, and highlight the informative region for improving the recognition power of the model.

This is a paper in 2019 CVPR with over 60 citations. (Sik-Ho Tsang @ Medium)
[Review] SPG: Self-Produced Guidance (Weakly Supervised Object Localization)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Object Localization, Convolutional Network, Deep Learning, Weakly Supervised

[Review] SPG: Self-Produced Guidance (Weakly Supervised Object Localization)

Learning Process of Self-Produced Guidance (SPG)

In this story, Self-produced Guidance for Weakly-supervised Object Localization, SPG, by University of Technology Sydney, and University of Illinois Urbana-Champaign, is shortly presented.

Weakly Supervised Object Localization (WSOL) is to have object localization while without object bounding box labels, but with only image-level label, for training. In this story:

Self-produced Guidance (SPG) masks are proposed, which separate the foreground i.e., the object of interest, from the background to provide the classification networks with spatial correlation information of pixels.

are proposed, which to provide the classification networks with spatial correlation information of pixels. The SPG masks are progressively learned and auxiliary supervised for further improvement in producing high-quality object localization maps.

This is a paper in 2018 ECCV with over 70 citations. (Sik-Ho Tsang @ Medium)
[Review] Park CVPR’17 / DHCF / DHDE: Multi-scale Deep and Hand-crafted Features for Defocus Estimation (Blur Detection)
By: Sik-Ho Tsang
Tags: Blur Detection, Artificial Intelligence, Convolutional Network, Deep Learning

[Review] Park CVPR’17 / DHCF / DHDE: Multi-scale Deep and Hand-crafted Features for Defocus Estimation (Blur Detection)

In this story, A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation, Park CVPR’17, by KAIST, and Tencent YouTu Lab., is presented. Some papers cited this method as the name of DHCF or DHDE. In this paper:

Hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture, are extracted.

A neural network classifier followed by a probability-joint bilateral filter are used to generate the final defocus map.

This is a paper in 2017 CVPR with over 40 citations. (Sik-Ho Tsang @ Medium)
EfficientNet 論文閱讀
Tags: Image Classification, Efficientnet, Artificial Intelligence, Convolutional Network, Deep Learning

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What I Didn’t Know I Didn’t Know About Convolutional Neural Networks
By: S. T. Lanier
Tags: Introduction, Image Classification, Cnn, Convolutional Network, Data Science

What I Didn’t Know I Didn’t Know About Convolutional Neural Networks

Left: a typical representation of a fully connected neural network. Right: two representations of convolutional neural networks. All made with NN-SVG.

Most people coming to convolutional neural networks (CNNs) have already been exposed to vanilla, fully connected neural networks, also known as multilayer perceptrons. If you’re anything like me, this can lead to a false sense of security because even though they’re both nominally neural networks, the challenges associated with each are different, and what you’re used to doing with a classic neural network (not that CNNs aren’t classic) isn’t what you’ll be doing with a CNN. In fact, a lot of the terrain surrounding CNNs won’t even be on your radar.

But now that you’ve tinkered around with some neural networks, maybe you’re ready to start playing with image data, try to tell some cats from some dogs. To do that, you’d be much benefited by using a CNN, whose most iconic application is probably in image classification. Below is enough information to get you started thinking about all the things you didn’t know you needed to think about. Examples and documentation are for Keras. If you start to feel the pressures of information overload, save this information and come back to it because it is a lot of information — everything I had to learn to start feeling comfortable with the CNN scene, in fact.

60% chance cat, 40% chance dog. 100% chance Nedjem. Photo by Diver Zhang on Unsplash.

Visualized intermediate activations from a CNN fit to Japanese kuzushiji characters. Photo by Author.

And to send us off, here’s an example of a simple CNN, from start to fit.
[Paper] ACoL: Adversarial Complementary Learning (Weakly Supervised Object Localization)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Object Localization, Convolutional Network, Deep Learning, Weakly Supervised

[Paper] ACoL: Adversarial Complementary Learning (Weakly Supervised Object Localization)

ACoL: Adversarial Complementary Learning

In this story, Adversarial Complementary Learning for Weakly Supervised Object Localization, ACoL, by University of Technology Sydney, University of Illinois Urbana-Champaign, and National University of Singapore, is presented. In this paper:

Weakly Supervised Object Localization (WSOL) is to have object localization while without object bounding box labels, but with only image-level label, for training.

A simple network architecture is proposed which includes two parallel-classifiers for object localization. Classification while also dynamically localizing some discriminative object regions during the forward pass.

An adversarial learning, the two parallel-classifiers are forced to leverage complementary object regions for classification and can finally generate integral object localization together.

This is a paper in 2018 CVPR with over 170 citations. (Sik-Ho Tsang @ Medium)
[Paper] Dense-Gated U-Net (DGNet): Brain Lesion Segmentation (Biomedical Image Segmentation)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Convolutional Network, Biomedical Segmentation, Unet, Deep Learning

[Paper] Dense-Gated U-Net (DGNet): Brain Lesion Segmentation (Biomedical Image Segmentation)

In this story, A Dense-Gated U-Net for Brain Lesion Segmentation, Dense-Gated U-Net (DGNet), by Peking University Shenzhen Graduate School, Peking University, and Macau University of Science and Technology, is shortly presented. In this paper:

A Dense-Gated UNet (DGNet), which is a hybrid of Dense-gated blocks and U-Net , is proposed.

, is proposed. DGNet can achieve weighted concatenation and suppress useless features.

This is a paper in 2020 VCIP where the conference has just been over this month. (Sik-Ho Tsang @ Medium)
[Paper] CAM: Learning Deep Features for Discriminative Localization (Weakly Supervised Object Localization)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Object Localization, Convolutional Network, Deep Learning, Weakly Supervised

[Paper] CAM: Learning Deep Features for Discriminative Localization (Weakly Supervised Object Localization)

Class Activation Mapping (CAM)

In this story, Learning Deep Features for Discriminative Localization (CAM), by MIT, is presented. In this story:

Global Average Pooling (GAP) is used at the near end of the network.

is used at the near end of the network. By using GAP, Class Activation Mapping (CAM) technique is proposed for Weakly Supervised Object Localization (WSOL)

technique is proposed for Weakly Supervised Object Localization (WSOL) Object localization and image classification are performed in a single forward-pass.

As described in one 2020 CVPR WSOL paper, it’s surprising that it’s still one of the state-of-the-art WSOL approaches with superior performance. This is a paper in 2016 CVPR with over 3000 citations. (Sik-Ho Tsang @ Medium)
Fundamentals of ConvNets
By: Hussain Safwan
Tags: Machine Learning, Cnn, Convolutional Network, Computer Vision

ConvNets are at the heart of image recognition and computer vision. One of the most fascinating things about this is how a sequence of raw mathematical operations like multiplication and addition goes all the way to process and recognize complex visuals. In this episode, we talk about a few fundamental concepts of computer vision, define and illustrate the working procedure of ConvNets and jot down some technical terms associated with that. First, let's talk of the idea since they are bulletproof(!)

The idea

The idea is simple, detect what’s inside an image, playback video, or a live recording (since all videos are at the most rudimentary level a sequence of fast-changing images). Technically speaking, computer vision is the science of programming a computer to gain a high-level understanding of digital imagery by replicating the working strategy of the human visual system. We use a variation of artificial neural networks called Convoluted Neural Network to achieve this. From here on, I’ll be referring to the technology as CNN. The core task of a CNN is to detect the valuable features in an image so that the image can itself be represented mathematically in the form of a vector of numerical figures which can, in turn, be compared against that of a labelled image to confirm to which class the subject belongs or what's in it depending on the initial requirements.

Now, CNN may have numerous layers, each of a different type — which I’ll not be discussing in this episode. The layer we’ll be focusing here today is the convolution layer.

The Convolution Layer

The convolution layer as the name suggests performs the convolution operation. To begin with, let point out the fact that digital images arrays of numbers (typically ranging between 0 to 256) denoting the colour combination of each pixel. For instance, an1 KB grayscale square image is in binary an array of size 1024 x 1024. Please note that coloured images use a third dimension called the colour channel and that for simplicity, we’ll be dealing with grayscale images in this lecture.

The primary job of a convolution layer is to detect significant features in an image. We begin with edge detection. An edge (no, not the WWE one!) in an image is a line, straight or curved, both sides which have a sharp change colour contrast. To detect one, a filter window is slid over the original image that attempts to look for an edge within its scope. Here’s how the convolution is done,

Take an image of dimensions nh x nw

A filter of fh x fw

Slide the filter over the image, one step at a time, and on each step, perform an element-wise multiplication of each element of the image with that of the filter, within the scope of the window and finally add the products and store the sum in a 2d resultant array, we’ll talk about the dimensions of the resultant array in a short while.

Here’s a pictorial view,